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Predictors of perinatal death in the presence of missing data: A birth registry-based study in northern Tanzania.

ABSTRACT: BACKGROUND:More than five million perinatal deaths occur each year globally. Despite efforts put forward during the millennium development goals era, perinatal deaths continue to increase relative to under-five deaths, especially in low- and middle-income countries. This study aimed to determine predictors of perinatal death in the presence of missing data using birth registry data from Kilimanjaro Christian Medical Center (KCMC), between 2000-2015. METHODS:This was a retrospective cohort study from the medical birth registry at KCMC referral hospital located in Moshi Municipality, Kilimanjaro region, northern Tanzania. Data were analyzed using Stata version 15.1. Multiple imputation by fully conditional specification (FCS) was used to impute missing values. Generalized estimating equations (GEE) were used to determine the marginal effects of covariates on perinatal death using a log link mean model with robust standard errors. An exchangeable correlation structure was used to account for the dependence of observations within mothers. RESULTS:Among 50,487 deliveries recorded in the KCMC medical birth registry between 2000-2015, 4.2% (95%CI 4.0%, 4.3%) ended in perinatal death (equivalent to a perinatal mortality rate (PMR) of 41.6 (95%CI 39.9, 43.3) deaths per 1,000 births). After the imputation of missing values, the proportion of perinatal death remained relatively the same. The risk of perinatal death was significantly higher among deliveries from mothers who resided in rural compared to urban areas (RR = 1.241, 95%CI 1.137, 1.355), with primary education level (RR = 1.201, 95%CI 1.083, 1.332) compared to higher education level, with <4 compared to ?4 antenatal care (ANC) visits (RR = 1.250, 95%CI 1.146, 1.365), with postpartum hemorrhage (PPH) (RR = 2.638, 95%CI 1.997, 3.486), abruption placenta (RR = 4.218, 95%CI 3.438, 5.175), delivered a low birth weight baby (LBW) (RR = 4.210, 95%CI 3.788, 4.679), male child (RR = 1.090, 95%CI 1.007, 1.181), and were referred for delivery (RR = 2.108, 95%CI 1.919, 2.317). On the other hand, deliveries from mothers who experienced premature rupture of the membranes (PROM) (RR = 0.411, 95%CI 0.283, 0.598) and delivered through cesarean section (CS) (RR = 0.662, 95%CI 0.604, 0.724) had a lower risk of perinatal death. CONCLUSIONS:Perinatal mortality in this cohort is higher than the national estimate. Higher risk of perinatal death was associated with low maternal education level, rural residence, <4 ANC visits, PPH, abruption placenta, LBW delivery, child's sex, and being referred for delivery. Ignoring missing values in the analysis of adverse pregnancy outcomes produces biased covariate coefficients and standard errors. Close clinical follow-up of women at high risk of experiencing perinatal death, particularly during ANC visits and delivery, is of high importance to increase perinatal survival.

SUBMITTER: Mboya IB

PROVIDER: S-EPMC7161952 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Predictors of perinatal death in the presence of missing data: A birth registry-based study in northern Tanzania.

Mboya Innocent B IB Mahande Michael J MJ Obure Joseph J Mwambi Henry G HG

PloS one 20200416 4

<h4>Background</h4>More than five million perinatal deaths occur each year globally. Despite efforts put forward during the millennium development goals era, perinatal deaths continue to increase relative to under-five deaths, especially in low- and middle-income countries. This study aimed to determine predictors of perinatal death in the presence of missing data using birth registry data from Kilimanjaro Christian Medical Center (KCMC), between 2000-2015.<h4>Methods</h4>This was a retrospectiv ...[more]

PMID: 32298332

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Project description:BackgroundPreterm 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.MethodsWe 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.ResultsThe 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).ConclusionsThe 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.

Project description:ObjectiveWe aimed to determine the key predictors of perinatal deaths using machine learning models compared with the logistic regression model.DesignA secondary data analysis using the Kilimanjaro Christian Medical Centre (KCMC) Medical Birth Registry cohort from 2000 to 2015. We assessed the discriminative ability of models using the area under the receiver operating characteristics curve (AUC) and the net benefit using decision curve analysis.SettingThe KCMC is a zonal referral hospital located in Moshi Municipality, Kilimanjaro region, Northern Tanzania. The Medical Birth Registry is within the hospital grounds at the Reproductive and Child Health Centre.ParticipantsSingleton deliveries (n=42 319) with complete records from 2000 to 2015.Primary outcome measuresPerinatal death (composite of stillbirths and early neonatal deaths). These outcomes were only captured before mothers were discharged from the hospital.ResultsThe proportion of perinatal deaths was 3.7%. There were no statistically significant differences in the predictive performance of four machine learning models except for bagging, which had a significantly lower performance (AUC 0.76, 95% CI 0.74 to 0.79, p=0.006) compared with the logistic regression model (AUC 0.78, 95% CI 0.76 to 0.81). However, in the decision curve analysis, the machine learning models had a higher net benefit (ie, the correct classification of perinatal deaths considering a trade-off between false-negatives and false-positives)-over the logistic regression model across a range of threshold probability values.ConclusionsIn this cohort, there was no significant difference in the prediction of perinatal deaths between machine learning and logistic regression models, except for bagging. The machine learning models had a higher net benefit, as its predictive ability of perinatal death was considerably superior over the logistic regression model. The machine learning models, as demonstrated by our study, can be used to improve the prediction of perinatal deaths and triage for women at risk.

Project description:BackgroundWhile factors that drive early mortality among people living with HIV (PLWH) initiating antiretroviral therapy (ART) in sub-Saharan Africa (SSA) have been described, less is known about the predictors of long-term mortality for those with ART experience.MethodsPLWH and on ART attending two HIV treatment clinics in Moshi, Tanzania were enrolled from 2008 through 2009 and followed for 3.5 years. Demographic, psychosocial, and clinical information were collected at enrollment. Plasma HIV RNA measurements were collected annually. Cause of death was adjudicated by two independent reviewers based on verbal autopsy information and medical records. Bivariable and multivariable analyses were conducted using Cox proportional hazard models to identify predictors of mortality.ResultsThe analysis included 403 participants. The median (IQR) age in years was 42 (36-48) and 277 (68.7%) participants were female. The proportion of participants virologically suppressed during the 4 collection time points was 88.5%, 94.7%, 91.5%, and 94.5%. During follow-up, 24 participants died; the overall mortality rate was 1.8 deaths per 100 person-years. Of the deaths, 14 (58.3%) were suspected to be HIV/AIDS related. Predictors of mortality (adjusted hazard ratio, 95% confidence interval) were male sex (2.63, 1.01-6.83), secondary or higher education (7.70, 3.02-19.60), receiving care at the regional referral hospital in comparison to the larger zonal referral hospital (6.33, 1.93-20.76), and moderate to severe depression symptoms (6.35, 1.69-23.87).ConclusionsAs ART coverage continues to expand in SSA, HIV programs should recognize the need for interventions to promote HIV care engagement for men and the integration of mental health screening and treatment with HIV care. Facility-level barriers may contribute to challenges faced by PLWH as they progress through the HIV care continuum, and further understanding of these barriers is needed. The association of higher educational attainment with mortality merits further investigation.

Project description:BackgroundPreterm birth is a public health problem particularly in low- and middle-income countries especially in sub-Saharan Africa. It is associated with infant morbidity and mortality. Survivor of preterm suffers long term health consequences such as respiratory, hearing and visual problems as well as delivering preterm infants. Preterm birth also tends to recur in subsequent pregnancies. Little is known about recurrent rate of preterm birth and associated factors in Tanzania. This study aimed to determine the recurrence rate of preterm birth and associated factors among women who delivered at Kilimanjaro Christian Medical Centre (KCMC), in Northern Tanzania.MethodsA historic cohort study was designed using maternally-linked data from KCMC medical birth registry. Women who delivered 2 or more singletons were included. A total of 5,946 deliveries were analysed. Recurrence of preterm birth and associated risk factors were estimated using multivariable log-binomial regression model with robust standard error to account for repeated births from the same mother.ResultsOverall recurrent rate of preterm birth was 24.4%. The recurrence of early preterm birth was higher compared to late preterm birth (26.2% vs. 24.2%). Similar pattern of recurrence was observed for spontaneous and medically indicated preterm birth (13.5% vs. 10.9%, respectively). Previous preterm birth (RR;1.85, 95% CI: 1.49, 2.31), preeclampsia (RR;1.46, 95% CI: 1.07, 2.00), long inter-pregnancy interval (RR;1.22, 95% CI: 1.01, 1.49) and clinical subtypes (RR = 1.37, 95% CI: 1.00, 1.86) were important predictors for recurrent preterm birth.ConclusionRecurrence of preterm birth remains higher in this population. The rate of preterm recurrence was dependent of gestational age and sub-clinical subtype. Other factors which were associated with recurrence of preterm birth were previous preterm birth, preeclampsia and long inter-pregnancy interval. Early identification of high risk women during prenatal period is warranted.

Project description:To identify characteristics associated with delivery room clinical instability in at-risk infants. Prospective cohort study. Two perinatal centres in Melbourne, Australia. Infants born at ≥ 35+0 weeks' gestation with a first-line paediatric doctor requested to attend. Clinical instability defined as any one of heart rate < 100 beats per minute for ≥ 20 s in the first 10 min after birth, maximum fraction of inspired oxygen of ≥ 0.70 in the first 10 min after birth, 5-min Apgar score of < 7, intubated in the delivery room or admitted to the neonatal unit for respiratory support. Four hundred and seventy-three infants were included. The median (IQR) gestational age at birth was 39+4 (38+4-40+4) weeks. Eighty (17%) infants met the criteria for clinical instability. Independent risk factors for clinical instability were labour without oxytocin administration, presence of a medical pregnancy complication, difficult extraction at birth and unplanned caesarean section in labour. Decision tree analysis determined that infants at highest risk were those whose mothers did not receive oxytocin during labour (25% risk). Infants at lowest risk were those whose mothers received oxytocin during labour and did not have a medical pregnancy complication (7% risk).ConclusionsWe identified characteristics associated with clinical instability that may be useful in alerting less experienced clinicians to call for senior assistance early. The decision trees provide intuitive visual aids but require prospective validation.What is known• First-line clinicians attending at-risk births may need to call senior colleagues for assistance depending on the infant's condition. • Delays in effectively supporting a compromised infant at birth is an important cause of neonatal morbidity and infant-mother separation.What is new• This study identifies risk factors for delivery room clinical instability in at-risk infants born at ≥ 35+0 weeks' gestation. • The decision trees presented provide intuitive visual tools to aid in determining the need for senior paediatric presence.

Dataset Information

Predictors of perinatal death in the presence of missing data: A birth registry-based study in northern Tanzania.

Publications

Predictors of perinatal death in the presence of missing data: A birth registry-based study in northern Tanzania.

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