Prenatal and adolescent blood lead levels in South Africa: Child, maternal and household risk factors in the Birth to Twenty cohort

Introduction

The risk factors for lead exposure in developing countries have not been fully described. This study looks at child, maternal and household factors associated with increased risk of lead exposure at birth and at 13 years of age in the Birth to Twenty cohort.

Methods

Mothers were recruited from antenatal clinics in the Johannesburg-Soweto metropolitan area in 1990 (n=3273). Lead levels were analysed in cord blood collected at birth (n=618) and at 13 years (n=1546). Data on selected child, maternal and household factors were collected using a structured questionnaire in the third trimester and at 13 years of age. Statistical analyses were conducted to determine the associated risk factors.

Results

The mean blood lead level at birth was 5.85 μg/dl, and at 13 years of age it was 5.66 μg/dl. The majority of children had blood lead levels above 5 μg/dl (52% at birth and 56% at 13 years). At birth, being a teenage mother and having low educational status were strong predictors for elevated cord blood lead levels. Being a male child, having an elevated cord blood level, and lack of household ownership of a phone were significant risk factors for high blood lead levels at 13 years.

Conclusion

Significant associations found in the study point to the low socio-economic status of lead-affected mothers and children. These poor circumstances frequently persist into later childhood, resulting in continued high lead levels. Thus broader measures of poverty alleviation and provision of better education may help decrease the risk of exposure.     

A comparison of risk prediction methods using repeated observations: an application to electronic health records for hemodialysis

An increasingly important data source for the development of clinical risk prediction models is electronic health records (EHRs). One of their key advantages is that they contain data on many individuals collected over time. This allows one to incorporate more clinical information into a risk model. However, traditional methods for developing risk models are not well suited to these irregularly collected clinical covariates. In this paper, we compare a range of approaches for using longitudinal predictors in a clinical risk model. Using data from an EHR for patients undergoing hemodialysis, we incorporate five different clinical predictors into a risk model for patient mortality. We consider different approaches for treating the repeated measurements including use of summary statistics, machine learning methods, functional data analysis, and joint models. We follow up our empirical findings with a simulation study. Overall, our results suggest that simple approaches perform just as well, if not better, than more complex analytic approaches. These results have important implication for development of risk prediction models with EHRs. Copyright © 2017 John Wiley & Sons, Ltd.