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Exposure to lead during childhood could be considerably more harmful for cognitive growth and academic performance than earlier believed, based on a recent assessment led by data analyst Joe Feldman.
Childhood lead exposure typically stems from crumbling lead-containing paint, tainted soil, or aged water pipes — dangers that persist in numerous U.S. neighborhoods.
Elevated levels of lead in a child’s bloodstream have long been recognized as detrimental to intellectual capacity. However, akin to numerous other real-world datasets, the information establishing the connection between lead exposure and cognitive growth is convoluted and incomplete.
“It’s evident that lead poses a risk,” stated Feldman, an assistant professor of statistics and data science within Arts & Sciences at Washington University in St. Louis. “Yet, determining the extent of that correlation has been challenging because many children do not undergo exposure testing, resulting in a plethora of missing data points.”
To gain a clearer understanding of the risks, Feldman and his collaborators — Jerome Reiter from Duke University and WashU graduate Daniel Kowal (AB ’12) now at Cornell University — examined data from 170,000 fourth-grade pupils in North Carolina, aiming to correlate lead exposure with end-of-grade standardized assessment scores. “Though standardized test scores have their flaws, they serve as significant indicators of child development and are closely linked to academic achievements in high school and beyond,” Feldman remarked.
Adding to the complexity of the analysis, lead exposure data were absent for approximately 35% of these children due to North Carolina’s regulations, which require testing only if a child is presumed at risk, possibly due to residing in homes or neighborhoods with lead pipes.
“The absence of lead exposure data is not random,” Feldman noted. “In statistics, we refer to this kind of missing information as ‘nonignorable.’ It is crucial to resolve these deficiencies to capture the entire scenario.”
As detailed in the journal Bayesian Analysis, the research team employed advanced statistical methods to arrive at a disturbing revelation: If all children had undergone lead level assessments, the connection between lead exposure and academic performance would likely be even more pronounced than previously thought.
“We utilized our model to estimate the missing lead values, constructing complete datasets. Upon analyzing these datasets, we discovered a markedly stronger correlation between lead exposure and test scores,” Feldman explained. “It appears that we have been under-appreciating the negative effects of lead exposure on children’s educational success.”
In order to assess lead levels in students who had not been tested, the researchers referred to published data on lead exposure among children at the population level from the Centers for Disease Control and Prevention (CDC). They subsequently applied Bayesian statistical modeling — a technique frequently used to draw conclusions from incomplete datasets — to supplement the absent lead data. “Our model effectively reconciles the insights from the observable data with the CDC statistics, which aids in formulating credible predictions for the missing figures,” Feldman stated.
This study underscores the necessity for expanded lead testing and strategies to mitigate exposure. It also emphasizes the significance of revisiting incomplete datasets. “Bayesian analysis holds immense potential since it enables us to account for the uncertainty stemming from missing data. Nonetheless, models can solely learn from the data that is available,” Feldman remarked. “Creating a statistical model that can simultaneously harness unobserved information while considering the complexities within the data was quite a formidable task.”
Feldman is employing similar methodologies to assess the effectiveness of medical treatments for depression. “Electronic health records offer a wealth of information, yet the data is frequently disorganized and incomplete,” he said.
When a patient exhibits a positive response to medication, their healthcare provider may cease monitoring or documenting their symptoms, resulting in gaps. Concurrently, there is plentiful external information — from clinical trials and various analyses — regarding the effectiveness of different treatments. “We aim to develop models that can incorporate this external knowledge to enhance our understanding of the missing data,” he commented.
This general methodology could also assist in illuminating several other inquiries complicated by incomplete information.
“Statistical models should not be hindered by the absence of data in a specific dataset,” Feldman asserted. “Our research allows practitioners to easily incorporate external information to enhance decision-making and public health initiatives.”
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