Pure Earth’s first-of-its-kind prioritization tool allows users to compare exposure estimates across countries and indicators.
Awareness of lead poisoning as a global health crisis has grown dramatically, and funding has followed. Major initiatives including The Audacious Project, Coefficient Giving’s Lead Exposure Action Fund (LEAF), and the Bloomberg Philanthropies Lead Poisoning Prevention Initiative have brought a surge of new researchers, donors, and implementing organizations into the field, arriving from maternal and child health, cardiovascular disease prevention, and economic development. Governments that once had no national blood lead data are launching surveys. The Partnership for a Lead-Free Future has set an ambitious target of eliminating lead poisoning by 2040. The problem that spent decades on the margins of global health is finally getting the attention it deserves, and for good reason.
Lead exposure kills an estimated 3.5 million people every year, more than all active wars, natural disasters, road accidents, HIV, and malaria combined. It strips children of 765 million IQ points annually, at an estimated cost of $6 trillion in global GDP. Yet most countries still lack reliable primary data on exposure levels, and the estimates that do exist are scattered across indicators and geographies in ways that make comparison difficult. As the field grows and new actors look for ways to engage, making better use of the data we already have becomes as urgent as collecting more of it.
Pure Earth built the Prioritization Tool, hosted on LeadPollution.org, to allow donors, governments, researchers, and implementing organizations compare lead exposure estimates across countries using multiple indicators, including elevated blood lead levels, population-level IQ loss, and deaths attributable to cardiovascular disease, giving the field a common framework for understanding where the burden falls and how to prioritize action.
For the first time, there is an interactive way to prioritize and rank lead exposure estimates. A Ministry of Health can compare key health outcomes with other countries and build the internal case for prioritizing lead as a public health issue, while a Ministry of Finance can gather data on IQ loss and its effect on lifetime earnings and national GDP. Funders can understand how lead exposure is undermining other investments, and for organizations newer to the lead issue, it offers a starting point for understanding where burden is most acute and where their work might have the greatest impact.
The State of Lead Exposure Estimates
Over the past decade, global models, particularly those produced by the Institute for Health Metrics and Evaluation (IHME), have dramatically improved our understanding of the scale and distribution of lead exposure. Because most countries lack nationally representative blood lead surveys, IHME builds its estimates by synthesizing available blood lead data and extrapolating across geographies using indicators such as urbanization, socioeconomic development, and the timing of leaded gasoline phase-outs, based on the assumption that countries with similar characteristics tend to have similar exposure levels. These models are updated every 2 years as new survey data and health research become available. We now have country-level estimates of blood lead levels, prevalence above key thresholds, cardiovascular mortality, and population-level IQ loss attributable to exposure.
At the same time, platforms such as LeadPollution.org and the Partnership for a Lead-Free Future have made these estimates and information about country-level projects increasingly accessible through interactive maps that allow governments, researchers, and implementers to explore how lead exposure varies across countries. Information that was once scattered across reports and datasets could finally be centralized and visualized in one place. But a more fundamental challenge remained. Even with better data, turning those estimates into decisions about where to act was not straightforward.
Global burden estimates are powerful. They quantify what was previously invisible, and they make the case for action. But they do not tell decision-makers what to do next. A policymaker or funder asking, “Where should we act first?” faces a surprisingly complex problem. Countries can rank very differently depending on how burden is defined. Take two examples:
- A country with a very large population may rank high in total burden—such as total IQ loss or total number of children exposed—because many people are affected.
- Another country may rank high in per-capita exposure, where a smaller population experiences more intense or widespread exposure.
Both are valid but imply different priorities. For national decision-makers, the same tension appears when deciding which populations to reach first. In practice, this reflects a fundamental strategic choice: whether to prioritize total good done, maximizing the number of people who benefit, or to take a triage approach, focusing resources where exposure is most severe. The first emphasizes scale and aggregate impact; the second emphasizes severity of harm. Neither is inherently correct, and each leads to different conclusions about where to act first.
This tension is not unique to lead. It appears across global health and development: should we prioritize where most people are affected, or where the risk per person is highest? What is new in the case of lead is the growing availability of multiple, partially overlapping indicators, each capturing a different dimension of harm. This reflects the wide range of biological systems affected by lead exposure: it is a potent neurotoxin that impairs cognitive development in children, but it also contributes to cardiovascular disease in adults and can affect kidney function, the immune system, and reproductive and developmental health. Blood lead levels primarily reflect recent or ongoing exposure, while lead accumulated in bone serves as a long-term reservoir, integrating exposure over time. As a result, no single indicator fully captures the total burden of exposure.
How the Prioritization Tool Works
To bridge this gap, we developed an interactive prioritization tool designed to translate modeled estimates into usable insights about burden across countries for donors, governments, and international organizations weighing where to act next. Building on an , and combine them into a composite score for each country. approach explored by the Global Development Incubator (GDI), the tool allows users to select from eight indicators drawn from IHME’s Global Burden of Disease 2023 estimates and World Bank analyses, and combine them into a composite score for each country.
Rather than producing a single definitive ranking, the tool is intended to help users explore how rankings shift under different scenarios by combining indicators and adjusting their relative weights. Indicators fall into two broad categories: total burden measures, such as total IQ loss or cumulative population blood lead levels, and per-capita measures, such as the prevalence of elevated blood lead levels or IQ loss per child. To allow fundamentally different indicators to be compared and combined, each is first normalized independently onto a 1–10 scale based on its distribution across countries, using percentile or log-scaled methods depending on the data, an approach that helps preserve meaningful differences even where burdens vary dramatically across countries. The weighted scores are then aggregated into composite country rankings. Users can adjust the relative weight assigned to each indicator using simple sliders, and the map and country rankings update in real time to reflect those choices (Figure 1). The indicator set can expand over time as new data sources become available.
What the Prioritization Tool Can’t Tell You
Even with improved estimates and better tools, important gaps remain. Because there are limited or no nationally representative blood lead surveys available for most countries, estimates are still largely modeled. Since rankings are based on modeled estimates rather than primary survey data, they should not be used as a baseline for measuring progress over time. A country that significantly reduces exposure may not see that change reflected in the model for several years.
In addition, the tool captures the exposure side of the picture, not the response side. A country that appears to be high burden on the map may have recently passed new lead regulations, launched a national blood lead survey, or begun a major remediation program, none of which is reflected in the current estimates. The tool is most useful as a starting point for identifying where to look more closely, not as a definitive measure of current conditions.
Finally, burden is not the same as opportunity. A country with high lead exposure may not be the most tractable place to intervene. Factors such as regulatory capacity, dominant sources of exposure, and political context all influence where interventions can succeed. It is exceedingly challenging for a model to fully capture these realities.
From Ranking to Action
If better models are not enough, what is the next step? The answer may lie in how we use them. Rather than treating global rankings as prescriptions, we can use them as starting points for structured inquiry: Why does this country rank highly under one metric but not another? What does that tell us about exposure patterns? What additional data would change our confidence in this ranking?
This approach mirrors successful models in other areas of public health, where surveillance systems are used not just to measure burden, but to generate actionable hypotheses.
Where We Go Next
Lead exposure remains one of the most preventable public health crises globally. The solutions are known, the data increasingly strong, and the funding and political will beginning to follow. What has been missing is a shared framework for a field that keeps growing but is not yet working from the same map. The Prioritization Tool offers governments, funders, implementing organizations, and researchers a way to engage with the same evidence through their own priorities, making the underlying trade-offs visible and interactive. That is what the field needs to act strategically and in coordination.
The tool is a starting point, and we know it will get better with use. We welcome feedback, ideas, and collaboration from anyone working on this issue, because the more perspectives that shape it, the more useful it becomes for everyone.
Questions or ideas? You can contact us at [email protected].
