This exploratory report, prepared by Rethink Priorities (RP), presents a suggested revision of the Lead Impact Model (LIM), originally developed by Pure Earth (PE). The model remains a work in progress and is not ready to guide resource allocation decisions. We share it to promote transparency, encourage collaboration, and highlight key data and knowledge gaps.

The LIM was developed by PE to better understand the scale, drivers, and priorities of the global lead poisoning challenge. Its long-term goal is to quantify national health burdens from individual sources of lead exposure, thereby informing policy priorities and supporting cost-benefit analyses of interventions. The LIM uses cumulative population BLLs (cpBLLs)—the average BLL of a population multiplied by its size—as the key metric for quantifying lead poisoning burdens (Fuller et al., 2025).

RP’s contributions focus on one component of the LIM: estimating national health burdens from individual lead exposure sources using biokinetic modeling. These contributions were developed in close collaboration with PE, which continues to refine the model and progress health burden attribution of lead exposure sources. The approach outlined in this report is based on a simple, transparent linear biokinetic model, adapted from the US Environmental Protection Agency’s (US EPA’s) Adult Lead Methodology. However, the utility of the model is currently limited by major data and knowledge gaps. First, the LIM currently relies on data from PE’s Rapid Market Screening (RMS) study (Sargsyan et al., 2024) and some home-based assessments (Pure Earth, 2023). The RMS study measured the lead concentration of 5,000 consumer product samples across 25 low- and middle-income countries, but was not nationally representative. Second, the LIM does not include all sources of lead exposure, and is best suited for sources involving direct ingestion, such as food and spices. Indirect lead exposure sources are modelled using highly uncertain assumptions relating to the pathways of lead transfer, such as the rate of leaching from cookware and the rate of lead shedding from paint to dust. The modeling of exposures from environmental lead pollution remains undeveloped. Finally, data on how frequently populations are exposed to different lead sources is currently very limited. 

The issues of unrepresentative data and uncertain assumptions are compounded when extrapolating to national burden estimates. As a result, the model’s health burden outputs are currently too uncertain to inform prioritization or resource allocation decisions. 
While recognizing these shortcomings, we hope that this research will be helpful to the broader community. We expect that multiple iterations of the LIM will be needed to achieve reliable burden attribution; by regularly and transparently sharing versions for feedback, we hope to speed progress and ultimately arrive at more accurate outcomes. We encourage others to use our low-confidence parameter estimates as starting points from which to hone their own intuitions and identify promising avenues for further research and data collection. 

We emphasize that there is no safe threshold for lead exposure and advocate for the regulation of all sources, regardless of modeled burden. Nonetheless, quantifying burdens may help shape priorities. To make this feasible, we highlight key areas for further research: