Environmental Research 132 (2014) 379–383

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The pediatric burden of disease from lead exposure at toxic waste sites in low and middle income countries Kevin Chatham-Stephens a,n, Jack Caravanos b,c, Bret Ericson c, Philip Landrigan a,d, Richard Fuller c a

Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1057, New York, NY 10029, USA School of Public Health at Hunter College, City University of New York, 2180 Third Avenue, New York, NY 10035, USA c Blacksmith Institute, 475 Riverside Drive, Suite 860, New York, NY 10115, USA d Department of Pediatrics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1057, New York, NY 10029, USA b

art ic l e i nf o

a b s t r a c t

Article history: Received 29 November 2013 Received in revised form 28 March 2014 Accepted 5 April 2014 Available online 21 May 2014

Background: The impact of lead from toxic waste sites on children in low and middle income countries has not been calculated due to a lack of exposure data. We sought to calculate this impact in Disability Adjusted Life Years (DALYs). Materials and methods: Using an Integrated Exposure Uptake Biokinetic (IEUBK) model, we converted soil and drinking water lead levels from sites in the Blacksmith Institute's Toxic Sites Identification Program (TSIP) into mean blood lead levels (BLLs). We then calculated the incidence of mild mental retardation (MMR) and DALYs resulting from these BLLs. Results: The TSIP included 200 sites in 31 countries with soil (n ¼ 132) or drinking water (n ¼68) lead levels, representing 779,989 children younger than 4 years of age potentially exposed to lead. Environmental lead levels produced a range of BLLs from 1.56 to 104.71 mg/dL. These BLLs equated to an estimated loss of 5.41–8.23 IQ points, resulting in an incidence of MMR of 6.03 per 1000 population and 76.1 DALYs per 1000 population. Discussion: Soil and water lead levels at toxic waste sites predict BLLs that lower the intelligence quotient (IQ), with the resulting MMR potentially limiting individual- and country-level development. The preventable burden of disease produced by these sites highlights the need for toxic waste sites to be systematically identified, evaluated, and remediated. & 2014 Elsevier Inc. All rights reserved.

Keywords: Blood lead level Children Disability adjusted life year Low and middle income country Toxic waste site

1. Introduction Since lead has long-term health consequences, including the potential to impair cognitive development in children, the potential sources of lead in a community should be identified and addressed. With the decreasing use of leaded gasoline in the past decade in low and middle income countries (LMICs), lead from toxic waste sites and environmental “hotspots” may contribute relatively more to overall lead exposure in certain communities. While high income countries such as the United States (U.S.) have made progress in identifying,

Abbreviations: BLL, Blood lead level; DALYs, Disability adjusted life years; EPA, Environmental protection agency; IEUBK, Integrated exposure uptake biokinetic; LMICs, Low and middle income countries; MMR, Mild mental retardation; TSIP), Toxic sites identification program; U.S., United States; WHO, World Health Organization; YLD, Years lived with disability; YLL, Years of life lost n Corresponding author. Fax: þ 212 996 0407. E-mail address: [email protected] (K. Chatham-Stephens). http://dx.doi.org/10.1016/j.envres.2014.04.018 0013-9351/& 2014 Elsevier Inc. All rights reserved.

mapping, and evaluating these sites, LMICs have made less progress (Yáñez et al., 2002). Exposures to toxic waste sites in LMICs may be greater than in high income countries given the higher population densities around these sites; the lack of environmental controls to contain toxicant release; and the barriers to proper identification and tracking of toxic waste and industrial byproducts (Dutta et al., 2006; Misra and Pandey, 2005). Lead-induced mild mental retardation (MMR) and cardiovascular disease account for 0.6% of the global burden of disease quantified by disability adjusted life years (DALYs) (World Health Organization, 2009). Due to a paucity of exposure data and biomonitoring around toxic waste sites and environmental “hotspots,” the exact contribution of these sites to the global burden of disease from lead is unclear. In this analysis we characterize environmental exposures at these sites in LMICs, estimate the resulting blood lead levels (BLLs), and calculate the burden of disease resulting from lead-induced MMR.


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2. Materials and methods 2.1. Site identification We used sites from the Blacksmith Institute's Toxic Sites Identification Program (TSIP), an effort to identify, evaluate, and remediate toxic waste sites in low and middle income countries. The TSIP focuses on point-source, legacy sites, which were formerly active sites, as well as artisanal “hotspots” (e.g., informal lead battery recycling, small-scale gold mining). As part of each site evaluation, the Blacksmith Institute identifies the dominant chemical pollutant, the contaminated environmental medium, and the population at risk of exposure. The key pollutant is determined through prior environmental testing or the historical use of the property (e.g., lead for a battery recycling operation and hexavalent chromium for a tannery). If sampling results are not available from a government agency or academic partner, then up to five convenience samples are obtained. The highest value is selected for analysis to model a worst-case scenario. We previously outlined the TSIP methodology, which is a modified version of the Hazard Ranking System used in the U.S. Environmental Protection Agency's (EPA) Superfund program, and characterized the findings from 2095 sites cataloged in the TSIP (Ericson et al., 2013). In brief, the Blacksmith TSIP estimates the population at risk of exposure to the key pollutant through the specific environmental medium using local census projections, aerial photographs, and government interviews. Since the TSIP estimates the total exposed population, we calculated the child population by applying U.S. Census Bureau country-specific age distributions for each site (United States Census Bureau. International Data Base, 2012). For this analysis, we defined the key pollutant as lead, the environmental medium as soil or drinking water, and the population at risk as children between 6 and 48 months of age. We categorized countries in the TSIP data base into low income; lower-middle income; and upper-middle income (World Bank, 2012). The majority of site evaluations were conducted in 2010, so we selected 2010 as the year for the analysis. 2.2. Calculation of BLLs To calculate BLLs, we used the U.S. EPA's Integrated Exposure Uptake Biokinetic (IEUBK) Model for Lead in Children (IEUBKwin v1.1 build 11), which has been used in the U.S. EPA's Superfund program. The IEUBK Model is a validated tool that estimates the geometric mean of BLL from exposure to multiple sources of lead. We entered drinking water or soil lead levels for each site into the model and calculated mean BLLs for each site. In the IEUBK Model sites with soil lead levels greater than 38,550 mg/kg defaulted to 38,550 mg/kg, and sites with drinking water lead levels greater than 6000 μg/L defaulted to 6000 μg/L. We kept the default values for other inputs, including air lead concentrations, dietary intake of lead, water consumption rates, maternal BLL, alternate sources of lead, and bioavailability of lead. For example, the default values for dietary intake of lead are 5.53 mg/day, 5.78 mg/day, 6.49 mg/day, and 6.24 mg/day for children aged 0–11 months, 12–23 months, 24–35 months, and 36–47 months, respectively (White et al., 1998). Using the IEUBK Model we calculated a single mean BLL for each toxic waste site overall and BLLs for each of the following age groups: 6–12 months, 12– 24 months, 24–36 months, and 36–48 months. We then calculated the median BLL for each age group across all the sites. 2.3. Calculating impact on IQ and DALYs We calculated the loss of intelligence quotient (IQ) points resulting from the estimated BLLs and the DALYs resulting from incident cases of MMR. We entered each site's mean BLL from the IEUBK model into equations derived from the Schwartz 1994 meta-analysis and the 2005 pooled analysis by Lanphear et al. (2005). Both of these projects analyzed multiple studies of lead's impact on neurodevelopment, specifically IQ. Since Schwartz documented a loss of 2.57 IQ points with an increase in BLL from 10 to 20 mg/dL, we estimated the loss of IQ points by multiplying 0.257 by the BLL. Lanphear et al. (2005) documented a loss of IQ points equal to  2.70  ln (concurrent BLL). By entering a BLL into these equations, we produced two different estimates of IQ points potentially lost from lead exposure for each site. For all the sites combined, we then calculated two median values of IQ points lost – One estimated using the Schwartz formula and another estimated using the Lanphear formula. Using the mean BLL for each site from the IEUBK model, we also calculated the incidence of lead-induced MMR and the resulting DALYs with World Health Organization (WHO) spreadsheets (Fewtrell et al., 2003). By entering a mean BLL into the first spreadsheet, we obtained the incidence of MMR. The spreadsheet calculates the number of children just above the MMR threshold of 70 IQ points who would drop into the MMR range due to lead-induced loss of IQ points and presents the rate of lead-induced MMR per 1000 population. This spreadsheet incorporates a regional adjustment ratio for MMR since the incidence of noncongenital causes of MMR, such as anemia and meningitis, varies in different geographic regions. In addition, the spreadsheet calculates the percentage of individuals with BLLs greater than 5 mg/dL using the size of the population and

mean BLL from the population. By entering the incidence of MMR from the first WHO spreadsheet and population at risk of exposure into the second WHO spreadsheet, we calculated the resulting DALYs and DALYs per 1000 children. DALYs are a combination of Years Lived with Disability (YLD) and Years of Life Lost (YLL) from a disease or health state. To calculate YLD, which represents the morbidity from a disease, the duration of the disease in years is multiplied by a disability weight. The disability weight is a value from zero to one that estimates the severity of the disease. For example, periodontal disease has a disability weight of 0.001. MMR due to lead has a disability weight of 0.361, and a first-time stroke has a disability weight of 0.920 (World Health Organization, 2008). There is no disability weight associated with loss of IQ points that does not result in MMR (Fewtrell et al., 2003). For example, if an individual's IQ decreased from 85 to 81 due to lead, then this change would not be captured in DALYs. DALY calculations typically incorporate only the lead-induced loss of IQ points resulting in MMR (Fewtrell et al., 2003). A child who develops MMR from lead at the age of 2 years and has a life expectancy of 80 years accumulates 28.2 YLD (78 years  0.361). While the calculation of DALYs from lead typically involves calculating YLL, which represents the premature mortality from a disease, from increase in blood pressure and the resulting cardiovascular disease, we did not include this component since the analysis is limited to children younger than 4 years of age. Therefore, our DALY calculations represent solely the morbidity associated with lead exposure at these sites and do not incorporate the premature mortality associated with lead exposure.

2.4. Sensitivity analysis We performed a sensitivity analysis by calculating DALYs using different discount rates and age weights. Calculation of DALYs typically incorporates a 3% discount rate due to the societal preference of a healthy year of life now versus in the future, and non-uniform age weights due to the relative societal value of certain ages (Mathers et al., 2006). The notation DALYs(r,K) signifies where discount rate (r) and age weight (K) are used. We present our primary results in DALYs(3,1) including both the 3% discount rate and the full age weight. We also present DALYs(3,0) with only the 3% discount rate and DALYs(0,0) without any weighting. Since Hogan et al., 1998 recommend caution in relying on the IEUBK model when calculated BLLs exceed 30 mg/dL, we capped the maximum BLL from each site at 30 mg/dL and report the subsequent change in DALYs(3,1).

3. Results As of August 2012, the TSIP database contained 200 sites in 31 countries with soil (n ¼132) or drinking water (n¼ 68) lead levels. India (n¼ 32), the Philippines (n¼ 27), Indonesia (n ¼26), China (n ¼19), and the United Republic of Tanzania (n ¼ 17) had the most sites (Table 1). The total population of children between 6 and 48 months of age at risk of exposure to lead at these sites was 779,989, with a median population of 579 children per site. 409,748 (52.5%) of these children lived in low income countries; 303,872 (39.0%) lived in lower-middle income countries; and 66,369 (8.5%) lived in upper-middle income countries. Low income countries contained 39 sites with a median of 8520 children per site; lower-middle income countries contained 104 sites with a median of 588 children per site; and upper-middle income countries contained 57 sites with a median of 266 children per site (Table 2). Soil lead levels ranged from 55 to 543,809 mg/kg, with a median of 2016 mg/kg. Drinking water lead levels ranged from 5 to 124,800 μg/L, with a median of 500 μg/L. Based on input of these environmental lead levels into the IEUBK model, we estimated geometric mean BLLs from 1.56 to 104.71 mg/dL, with a median of 21.04 mg/dL (1st quartile ¼8.28, 3rd quartile ¼ 45.03). Table 3 presents the median BLL for each of the 4 age groups. 86% of children at these sites had BLLs greater than 5 mg/dL, with 75 sites predicted to have 100% of the BLLs greater than 5 mg/dL. By entering each site-specific BLL into the formulas from Schwartz (1994) and Lanphear et al. (2005) , the median loss of IQ points was 5.41 IQ points (1st quartile¼2.12, 3rd quartile¼11.59) and 8.23 IQ points (1st quartile¼5.7, 3rd quartile¼ 10.28), respectively. These reductions in IQ lead to an incidence of MMR of 6.03 per 1000 children (1st quartile¼2.46 per 1000, 3rd quartile¼8.05 per 1000), resulting in a total of 51,614 DALYs(3,1) and 76.1 DALYs(3,1) per 1000

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Table 1 Countries with lead-contaminated sites evaluated by the Blacksmith Institute's Toxic Sites Identification Program. Country



Number of sites

India Philippines Indonesia China United Republic of Tanzania Argentina Pakistan Uganda Peru Bangladesh Remainder of countries

South Asia East Asia & the Pacific East Asia & the Pacific East Asia & the Pacific Sub-Saharan Africa Latin America & the Caribbean South Asia Sub-Saharan Africa Latin America & the Caribbean South Asia

Lower-middle Lower-middle Lower-middle Upper-middle Low Low Lower-middle Low Upper-middle Low

32 27 26 19 17 9 9 9 7 6 39

Table 2 Lead-contaminated sites from the Blacksmith Institute's Toxic Sites Identification Program by income level and population.

Income category Low income Lowermiddle income Uppermiddle income

Population o 4 years

Median % of total exposed population per population site













Number of sites

children (1st quartile¼31.7 per 1000, 3rd quartile¼102.48 per 1000). The low income countries contributed 55.8 DALYs(3,1) per 1000; the lower-middle income countries 78.7 DALYs(3,1) per 1000; and the upper-middle income countries 81.2 DALYs(3,1) per 1000. 10 most affected sites yielded 33,598 DALYs(3,1), accounting for 65.1% of the total DALYs, while the 20 most affected sites yielded 41,565 DALYs(3,1), accounting for 80.4% of the total DALYs. Removal of the age weight yielded 44,475 DALYs(3,0), while removal of both the age weight and discount rate yielded 115,887 DALYs(0,0). Seventy-seven sites had mean BLLs greater than 30 mg/dL, assigning a maximum value of 30 mg/dL to these sites decreased the DALYs(3,1) from 51,614 to 50,852 (Table 4).

Table 3 Predicted pediatric blood lead levels at lead-contaminated sites in low and middle income countries. Age (months)

Median BLLa (mg/dL)

6–12 12–24 24–36 36–48

17.5 22.2 21.2 20.8

a Blood lead levels were calculated using the U. S. EPA IEUBK Model, which resulted in a mean BLL for each age group at each of 200 sites. We present the median BLL for each age group across 200 sites.

Table 4 Disability Adjusted Life Years (DALYs) from lead-induced mild mental retardation for children aged 6–48 months in low and middle income countries with leadcontaminated sitesa. DALYs(3,1)b DALYs(3,1) with BLL capc DALYs(3,0)d DALYs(0,0)e

51,614 50,852 44,408 115,714

4. Discussion

a DALYs calculated from projected BLLs estimated from environmental lead levels. b DALYs(3,1) including 3% discount rate and the full age weight. c DALYs(3,1) with the maximum BLL capped at 30 mg/dL. d DALYs(3,0) including 3% discount rate and no age weight. e DALYs(0,0) without any discount rate or age weight.

We calculated that almost 800,000 children younger than 4 years of age are potentially exposed to lead from 200 toxic waste sites and environmental “hotspots” in 31 countries in 2010. These environmental lead levels correlate with BLLs that have been associated with adverse effects on neurodevelopment, specifically loss of IQ points. The majority of sites were projected to have mean BLLs greater than 5 μg/dL, the reference level above which the U.S. Centers for Disease Control and Prevention (CDC) endorses actions to reduce ongoing exposure (Centers for Disease Control and Prevention, 2012). These BLLs result in an estimated loss from 5.41 to 8.23 IQ points, MMR incidence of 6.03/1000 population, and 51,614 DALYs(3,1) (ranging from 44,408 DALYs(3,0) to 115,714 DALYs(0,0)). Lead continues to be identified regularly at toxic waste sites and environmental “hotspots” throughout the world. The U.S. EPA estimates that greater than 300 Superfund sites in the United States are contaminated with lead (Environmental Protection Agency, 2012). van Geen et al. (2012) calculated that 1.6 million individuals in Peru live in proximity of an operational mine or legacy site and may be exposed to elevated soil lead levels. An

investigation of more than 2000 toxic waste sites in LMICs documented that lead was the primary pollutant at 458 sites (Ericson et al., 2013). Exposure to lead from toxic waste sites often falls disproportionately on children in LMICs and children of lower socioeconomic status in high income countries (Martuzzi et al., 2010). Approximately half of the population in our analysis lived in low income countries, with a median population of 8520 children per site. Fewtrell et al. (2004) documented that LMICs are disproportionately burdened by childhood lead poisoning. 40% of all children worldwide had BLLs greater than 5 μg/dL, with 90% of these children living in developing countries. The analysis by Fewtrell et al. (2004) further documented that DALYs from leadinduced MMR ranged from 0.7/1000 population in the region including Japan and Australia to 13.2/1000 population in the region including Argentina, Brazil, and Mexico. While the analysis by Fewtrell et al. (2004) focused on country-level estimates of BLLs and did not take into account “hotspots” such as toxic waste sites, our analysis focused on high-risk, small sub-populations within


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LMICs. These differences may explain the differences between the estimated DALY rates. There are potential neurodevelopmental and economic ramifications of chronic lead exposure at these sites. While a small to moderate decrease in IQ in an individual may not result in clinically apparent changes, substantial effects may arise when applied on a societal scale (Fewtrell et al. (2004). When the IQ curve is shifted downwards due to lead exposure, the number of children with MMR increases and the number of gifted children decreases (Bellinger (2007)). Grosse et al. (2002) proposed a causal model in which lead in the environment increases a child's BLL, which results in a lower IQ, which subsequently results in decreased individual productivity and lifetime earnings. Assuming that each 1 mg/dL increase in BLL reduces IQ by 0.185–0.323 points and that each 1 point decrease in IQ reduces individual productivity by 1.76–2.38%, Grosse et al., 2002 calculated that each birth cohort of 2 year olds in the United States earns an additional 110– 319 billion U.S. dollars in lifetime earnings due to the decline of mean BLL from 17.1 in 1976 to 2 in 1999. To the best of our knowledge, these relationships and economic estimates have not been reproduced in LMICs. There are number of limitations to the approach of our analysis. Due to a variety of factors, sites in the TSIP are not distributed equally throughout LMICs. 5 countries with the most sites comprise 60.5% of the entire sample and 21 countries have 3 or fewer sites. Even in countries with numerous site investigations, only a fraction of the existing toxic waste sites was evaluated. Unidentified and unscreened sites would likely add substantially to the estimated burden of disease. Within the screened sites there was a range of exposures and projected health effects, with 20 most affected sites accounting for greater than 80% of the total estimated DALYs. In addition, limited environmental sampling occurred at each site, forcing sampling results to be extrapolated to the entire site. Soil lead levels across each site are likely nonuniform, potentially leading to heterogeneous exposures as has been documented around Peruvian mining sites (van Geen et al., 2012). Our approach of using the highest environmental lead level to model a worst-case scenario may overestimate the outcomes in this analysis. Since actual BLLs were not obtained, we could not confirm completed pathways of exposure. This limitation is partially offset by using the IEUBK model. In a validation exercise, Hogan et al. (1998) found that the IEUBK model estimated BLLs within 1 μg/dL of the actual BLLs for children in 4 U.S. communities. While this model was developed primarily to project a BLL for a single residence, community-level BLLs can be estimated for sites with homogenous environmental lead levels (White et al., 1998). However, some sites had environmental lead levels high enough to produce BLLs greater than 30 mg/dL. At these levels, there is uncertainty regarding the exact contribution of environmental lead to BLLs (Hogan et al., 1998). In addition, the highest BLL in the analysis performed by Lanphear et al., 2005 was 61.5 mg/dL, while the highest BLL predicted in our analysis exceeded 100 mg/dL. We extrapolated the BLL–IQ relationship calculated from the relatively low BLLs in analyses by Schwartz (1994) and Lanphear et al. (2005) to the high BLLs predicted by the IEUBK model . Given that the exact relationship between BLLs at such high levels and IQ is unclear, we likely overestimate the impact on IQ in our analysis. Capping the projected BLL for all the sites at a maximum of 30 mg/dL decreased DALYs(3,1) from 51,614 to 50,852, resulting in 1.5% decline. Due to variety of factors, we were not able to include other health effects from lead, specifically cardiovascular disease due to lead‐induced increases in blood pressure. Since we kept the default values for various sources of lead with the exception of drinking water and soil lead, BLLs in this analysis may be

underestimated. For example, the IEUBK model uses estimates of dietary lead intake from the United States; dietary lead intake at the sites in our analysis may differ from these estimates (White et al., 1998). In addition, the prevalence of nutritional deficiencies, such as iron-deficiency anemia, may result in the estimated BLLs differing from actual BLLs at these sites. The population at risk was estimated through a variety of means, each of which introduces uncertainty into the final estimate. An assumption in MMR calculations is that IQ is normally distributed, so MMR calculations may be impacted if IQ is not distributed normally in any of the analyzed populations. These calculations may also be impacted if a country has an incidence of non-congenital disease resulting in MMR that differs from the regional value used in the WHO spreadsheet. Acknowledging these limitations, our analysis highlights the importance of assigning public health resources to systematically identify, evaluate, and remediate lead-contaminated toxic waste sites. Most LMICs do not have existing programs to achieve these goals, therefore lead exposure from these sites is likely to continue. International attention periodically focuses on high-profile cases in LMICs involving acute poisonings and fatalities, such as recent events involving artisanal gold mining in Nigeria and recycling of used lead-acid batteries in Senegal Haefliger et al. (2009); Lo et al. (2012).While these high- profile events may prompt remediation, lower-dose, chronic exposures may continue unabated at other sites.

5. Conclusions Despite being limited to only 200 toxic waste sites and environmental “hotspots” in 31 countries, our analysis documents that chronic exposure to lead from toxic waste sites in LMICs exerts a preventable burden of disease in children younger than 4 years of age. Soil and drinking water lead levels are high enough to produce BLLs that have been linked to a variety of detrimental effects on neurodevelopment, including loss of IQ points. Given the health, economic, and societal impact of lead, countries should create programs to systematically identify, characterize, and remediate these sites to reduce exposures. In addition, further research should measure environmental lead levels at toxic waste sites and BLLs in the exposed population to better define the impact of these sites. This research should focus on subpopulations that are uniquely vulnerable to the effects of lead, including pregnant females and children.

Role of the funding source The Toxic Sites Identification Program (TSIP) is implemented jointly with the United Nations Industrial Development Organization, and has been funded in part by the European Commission, Green Cross Switzerland, the World Bank, and the Asian Development Bank. The agencies that helped fund the TSIP had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. References Bellinger, D.C., 2007. Interpretation of small effect sizes in occupational and environmental neurotoxicology: individual versus population risk. Neurotoxicology 28 (2), 245–251. Centers for Disease Control and Prevention. Lead. 2012. Available: 〈http://www.cdc. gov/nceh/lead/〉 (accessed 21.11.12.). Dutta, S.K., Upadhyay, V.P., Sridharan, U., 2006. Environmental management of industrial hazardous wastes in India. J. Environ. Sci. Eng. 48 (2), 143–150.

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The pediatric burden of disease from lead exposure at toxic waste sites in low and middle income countries.

The impact of lead from toxic waste sites on children in low and middle income countries has not been calculated due to a lack of exposure data. We so...
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