Development, Trade and Environmental Justice: Decoupling Economic Growth from Lead Mortality in Africa

Development, Trade and Environmental Justice: Decoupling Economic Growth from Lead Mortality in Africa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Development, Trade and Environmental Justice: Decoupling Economic Growth from Lead Mortality in Africa Gift Fabolude, Charles Knoble, Anvy Vu, Isoken Aighewi, Danlin Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7161842/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Lead exposure remains a serious environmental health threat in Africa, especially for children. Amid economic growth, weak regulatory safeguards may exacerbate lead-related morbidity and mortality – the development paradox. This study investigates how socioeconomic development, infrastructure, and policy factors relate to premature deaths from lead exposure across African countries. We analyzed data for 52 African nations on premature deaths attributed to lead exposure (Global Burden of Disease 2021), alongside indicators including GDP, lead paint bans, public awareness (Google search index), vegetation cover (NDVI), import volume, and sanitation access. A multivariate log-linear regression assessed associations with lead-attributable mortality. Residual spatial autocorrelation is detected, and a spatial error model accounted for unobserved geographic effects. Guided by Environmental‑Justice and Pollution‑Haven theory, we test three propositions: (i) GDP–mortality coupling, (ii) trade‑mediated toxicity transfer, and (iii) infrastructure‑driven mitigation. The model finds that higher GDP was significantly associated with increased lead mortality ( \(\:\beta\:=0.557,\:p<0.001\) ), as was import volume ( \(\:\beta\:=0.342,\:p=0.008\) ). Improved sanitation correlated with lower mortality ( \(\:\beta\:=-0.019,\) \(\:p<0.001\) ). Public awareness showed a marginally significant protective effect ( \(\:p=0.057\) ). Lead paint regulation and vegetation cover were not significantly associated. The spatial error model improved fit and identified spatially correlated risks ( \(\:\lambda\:\approx\:0.50,\:p<0.001\) ). Our study for the first time suggests that in Africa, economic development without environmental safeguards may elevate lead exposure – a “lead exposure paradox.” Globalization facilitates hazardous imports (e.g., e-waste), compounding risks. Basic infrastructure like sanitation appears protective. These findings call for integrated industrial, trade and health policies aligned with Sustainable Development Goals (SDGs) 3, 8 and 12. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Health sciences/Risk factors Lead Exposure Environmental Health Africa Development Paradox Mortality Public Health Policy Figures Figure 1 Figure 2 1. Introduction Lead, a toxic heavy metal, remains a serious environmental health threat in many parts of Africa. Unlike in high-income countries where aggressive regulations have dramatically reduced lead exposure, African populations continue to experience significant lead burdens​ (Oulhote 2015 ; Bede-Ojimadu, Amadi, et Orisakwe 2018). Currently, no level of lead exposure is considered safe, and even low levels can harm health, particularly impacting neurodevelopment ​(Bede-Ojimadu, Amadi, et Orisakwe 2018; Vorvolakos, Arseniou, et Samakouri 2016; Danlin Yu 2025b ). From a social‑science standpoint, Africa exemplifies a “pollution haven” dynamic wherein regulatory gaps externalize health costs (Copeland 2008 ). At the same time, Environmental Kuznets Curve (EKC) theory predicts a turning‑point once institutions strengthen (Kordas et al. 2018 ). This study positions lead mortality at that intersection, posing two questions: (1) How do economic and trade indicators jointly predict mortality? (2) Which infrastructural or regulatory levers disrupt that coupling? Lead toxicity poses a particularly severe threat to vulnerable populations, especially children, whose developing nervous systems are highly susceptible to permanent damage. Exposure during critical developmental windows can result in irreversible neurocognitive deficits, reduced IQ, behavioral problems (such as decreased attention span and increased frustration), and diminished lifetime earning potential (Lanphear et al. 2005). The sources of lead exposure across Africa are diverse and widespread. They include deteriorating lead-based paints, contaminated soils, informal used lead-acid battery (ULAB) recycling, informal electronic waste (e-waste) processing, lead-glazed ceramics and cookware, lead-soldered food cans, contaminated spices (especially turmeric adulterated with lead chromate), and traditional medicines or cosmetics containing lead (A. Mathee 2014 ). In many regions, rapid urbanization and industrialization have occurred without adequate environmental safeguards or enforcement capacity, potentially increasing population exposure to this neurotoxicant. The pace of urbanization across many African nations has often outpaced regulatory frameworks, resulting in land use patterns that increase population exposure to lead (Suk et al. 2016 ; Dórea 2019 ). For instance, the common practice of situating repair shops, small-scale manufacturing, and informal recycling operations within densely populated residential areas creates direct pathways for lead exposure (Poudel et al. 2024 ). However, the relationship between urbanization and lead exposure can be complex, with some studies reporting higher blood lead levels (BLLs) in rural settings potentially linked to factors like specific agricultural practices, geophagy, or nutritional deficiencies enhancing lead absorption (A. Mathee et al. 2014 ; Forsyth et al. 2019 ). In countries like Nigeria and Ghana, economic necessity drives informal recycling sectors (ULAB and e-waste) that expose vulnerable populations, particularly children, to dangerous levels of lead. Children from impoverished households often scavenge damaged electronic goods from dumpsites receiving e-waste, sometimes imported from developed countries (Abogunrin-Olafisoye et Adeyi 2025; Püschel et al. 2024 ). These young scavengers dismantle items like computers, televisions, mobile phones, and batteries, often using rudimentary tools or bare hands without protective equipment, leading to direct exposure to lead dust and fumes from burning components. Informal recycling frequently occurs near homes, creating continuous exposure pathways (Lebbie et al. 2021). BLLs in children from these communities often significantly exceed international safety thresholds (A. Mathee 2014 ; Danlin Yu 2025b ). This exposure contributes to developmental delays, cognitive impairments, and behavioral problems, potentially reinforcing cycles of poverty (Desye et al. 2023 ; Pascale et al. 2016 ; Püschel et al. 2024 ). The mining industry, particularly artisanal and small-scale mining (ASM), also represents a significant source of lead exposure for workers and communities in countries like Nigeria, Zambia, South Africa, and Tanzania (Brunnschweiler, Karapetyan, et Lujala 2024; Ondayo et al. 2024 ). Ore processing often occurs with minimal safety precautions, exposing miners (including children in some ASM operations) to lead-laden dust, which can be carried home. The 2010 lead poisoning disaster in Zamfara, Nigeria, linked to informal gold mining releasing associated lead minerals, resulted in hundreds of child deaths and widespread poisoning (Lo et al. 2012 ). Even in more formalized operations, inadequate enforcement can lead to contamination of air, soil, and water from mining tailings and waste, creating long-term exposure risks for nearby communities (Lo et al. 2012 ; Tirima et al. 2016 ). Despite growing recognition of lead’s public health significance, understanding the interplay of factors influencing lead exposure and mortality in Africa remains limited. This study aims to provide insights through a commentary review and a cross-country analysis examining how economic development, regulatory policies, environmental conditions, public awareness, and infrastructure quality collectively shape lead exposure mortality patterns. We specifically investigate the relationship between premature deaths attributed to lead exposure (using GBD estimates) and potential determinants: GDP, lead paint ban status, public awareness (proxied by Google search trends), vegetation coverage (NDVI), imports, and sanitation infrastructure. This study aims to contribute to environmental health policy by identifying potential leverage points for intervention and highlighting the “development paradox” where economic progress without adequate environmental health safeguards may increase the lead burden. By examining this complex issue, we hope to inform more effective, context-specific strategies to reduce lead exposure in the Africa continent. The manuscript is organized as follows. After this introduction section, we will briefly review lead exposure in African countries, existing governmental regulation and intervention, and its economic and financial implications. Data and methods are introduced next. Results from the analysis are presented in the fourth section, which is followed by a detailed discussion. We conclude our study with a summary and reflection of the limitations presented in the current study, aiming for better exploration in the future. 2. Literature review 2.1 Extent of Lead Exposure in African Countries BLLs in African populations, especially children, are alarmingly high in many settings (Moya-Alvarez et al. 2016 ). Studies consistently show a large proportion of children with BLLs above the widely recognized reference level of 5 µg/dL. For example, surveys in the 1990s found over 90% of children in Cape Province, South Africa had BLLs ≥ 10 µg/dL (Nriagu, Blankson, et Ocran 1996). Beyond blood measurements, the burden of disease attributable to lead is significant. In 2017, lead exposure was estimated to account for over 54% of developmental intellectual disability burden in children under 5 in Sub-Sahara Africa (SSA) (Bede-Ojimadu, Amadi, et Orisakwe 2018). Lead also contributes substantially to premature deaths via cardiovascular and other diseases. However, surveillance is limited. Except perhaps for South Africa, most African nations lack systematic data on population lead levels (Oulhote 2015 ). This data scarcity likely means the true extent of lead poisoning is under-recognized. 2.2 Government Regulations and Interventions African governments, often with international partners, have undertaken interventions against lead exposure. The most successful has been the phase-out of leaded gasoline. Spurred by global initiatives led by UNEP’s Partnership for Clean Fuels and Vehicles, all African countries eliminated lead additives in petrol between the early 2000s and 2021 (UNEP 2021 ). Consequently, air lead levels dropped, and BLLs in recent generations are lower than in the 1980s–90s (Angela Mathee et al. 2006 ). Lead paint regulation is another critical area. Lead paint remains widely available, but progress is being made. South Africa pioneered regulation in 2009 (SAMRC 2019 ).​ By 2015, officials from 15 African countries aimed for a 90 ppm lead limit by 2020, the level recommended by the WHO/UNEP Global Alliance to Eliminate Lead Paint​ (UNEP 2015 ). Regional bodies like the East African Community and Economic Community of West African States (ECOWAS) moved towards harmonized standards. However, as of late 2021, only about 7 African countries (13%) had legally binding controls, often with varying standards. Countries like Kenya, Tanzania, Ethiopia, Cameroon, Côte d’Ivoire, Ghana, and Morocco have made recent progress. Enforcement remains a challenge even where laws exist, requiring capacity to monitor compliance for both domestic and imported paints (UNEP 2020 ; SAMRC 2019 ). Addressing lead in consumer products (toys, ceramics, cosmetics) and contaminated sites involves mixed interventions. Some governments have import controls or standards (e.g., Tunisia, Morocco on kohl; Nigeria on pottery glazes), but enforcement varies. International agencies (WHO, UNICEF) and NGOs have run awareness programs and product testing campaigns (e.g. checking lead content in paints and toys in markets, then publicizing the results to spur government action) with mixed success (UNEP 2017 ). Emergency clean-ups followed acute poisoning events, like in Zamfara (Nigeria) in 2010, involving government and international partners (WHO, MSF) for soil remediation and chelation treatment 26 (UNEP, 2017 ). Similarly, Senegal removed contaminated soil and relocated informal battery recycling after a 2008 tragedy (UNEP 2017 ). 2.3 Financial and Economic Implications Lead poisoning presents significant financial and economic challenges in Africa, exacerbated by policy weaknesses, the large informal sector, and rapid urbanization. Informal activities like ULAB and e-waste recycling contribute substantially to exposure due to inadequate regulation and enforcement (Oulhote 2015 ). Economic development, urbanization and industrialization, if pursued without adequate environmental protection, can increase lead exposure risks (Kordas et al. 2018 ; Pennington et al. 2024). The economic burden is likely substantial but understudied in Africa. In the US, childhood lead poisoning incurs estimated annual costs of $ 76.6 billion in healthcare and productivity losses (Attina et Trasande 2013), with adult occupational exposure adding further costs (Levin 2016 ). Given likely much higher exposure levels in many African settings, the economic toll is potentially far greater. Investing in lead exposure prevention is an economic imperative, not just a public health necessity. Cost-benefit analyses, primarily from high-income settings but conceptually relevant globally, demonstrate substantial returns. Studies estimate that every $ 1 invested in lead hazard control yields returns ranging from $ 17 to $ 221 (Gould 2009 ). These gains arise from avoided healthcare costs, reduced special education needs, increased productivity and lifetime earnings, higher tax revenues, and lower crime rates (Gould 2009 ). Despite these potential returns, many African governments face financial constraints and competing development priorities, hindering implementation and enforcement of lead regulations. A critical issue is addressing the informal sector involved in activities like ASM or ULAB/e-waste recycling, which are often driven by poverty (Desye et al. 2023 ). Outright bans risk worsening economic hardship. A preferable approach involves providing financial and technical incentives for transitions to safer practices, such as microfinance, support for safer recycling economies, and employment in remediation, ensuring health protection aligns with livelihood needs. Overcoming these challenges requires strategic integration of regulation, investment, and economic incentives to make lead mitigation economically viable and sustainable (Danlin Yu 2025b ). 2.4 DevelopmentEnvironment Theories Building on the preceding review of Africa’s lead-exposure burden and regulatory gaps, we now synthesize the three complementary lenses, Pollution Haven, Environmental Kuznets Curve (EKC) and Environmental Justice (EJ), to frame the study’s hypotheses. Pollution-Haven logic posits that multinational firms redirect hazardous production to jurisdictions where environmental standards and enforcement costs are lowest. In the African context, expanding imports of used batteries, ewaste and lowgrade pigments effectively externalize toxic risks from the global North to the global South. As a result, GDP growth that is heavily trade‐mediated can raise lead‐related mortality even when domestic industrial output remains modest. The EKC hypothesis refines this view by introducing an institutional turning point: early-stage growth often intensifies pollution, but beyond a threshold of income, governance capacity and citizen demand for clean environments strengthen, bending the curve downward. Whether African economies have reached or can accelerate toward this governance inflection is a central empirical question. Finally, Environmental-Justice theory emphasizes spatial and social inequities in the distribution of environmental harms. Colonial trade corridors, urban-rural divides and informalsector recycling concentrate lead exposure among children and lowincome populations, turning aggregate growth into a regressive health burden. Synthesizing these perspectives, we advance a trademediated toxicity pathway: rising GDP and import volumes elevate lead risk until governance, infrastructure (e.g., sanitation) and public awareness (proxied by Google-Trends searches) catch up. This integrative lens motivates our three propositions, namely, GDP–mortality coupling, import‐driven toxicity transfer and infrastructurebased mitigation, and shapes the spatial–econometric strategy we will detail next. 3. Data and methods The extent of lead exposure in Africa necessities the analysis of its determinants for better lead prevention policies. This study explores the relationships between lead exposure mortality and multiple socioeconomic and environmental factors across African nations. By examining GDP, lead paint regulations, public awareness metrics, imports, greenery, and sanitation infrastructure, we seek to identify factors associated with lead exposure deaths. This multifactorial approach aims to move beyond simplistic narratives, understanding how development pathways, environmental conditions, and policies collectively shape lead exposure risk. Our findings intend to provide evidence-based guidance for interventions, recognizing diverse contexts and potential unintended consequences of economic development lacking adequate environmental health safeguards. 3.1 Data sources Data used in this analysis is summarized in Table 1 . Each of these variables captures a distinct dimension, ranging from health burden, economic drivers, environmental mediators, infrastructure capacity, policy interventions, societal awareness, and trade pathways, that together form a comprehensive model for explaining cross-country variation in lead exposure mortality in Africa. While it is our intention to include the most recent data available, we have to adjust for the different years that there are sufficient data. Detailed descriptions of the data source follow. Table 1 Predictor Variable Summary Variable Definition/Operationalization Data Source Unit Year(s) Used Hypothesized Relationship with Lead Mortality Dependent Variable LED Number of premature deaths attributed to lead exposure IHME Global Burden of Disease (GBD) Study (via Our World in Data) Count 2021 N/A Independent Variables GDP Gross Domestic Product estimates Natural Earth / World Bank (Authors to specify exact source & metric, e.g., GDP per capita PPP) Millions USD 2021 +/- (Ambiguous: EKC suggests initial +, then -; Development paradox suggests +) Lead Paint Ban Status (Lead paint) Binary: 1 = Legally-binding controls exist; 0 = No such controls exist WHO Global Health Observatory 0/1 2023 - (Effective regulation should reduce mortality) Public Awareness (Google index) Average relative search volume for "lead exposure" + "lead poisoning" (weighted for internet penetration) Google Trends Index (0-100, relative) 2004–2021 (average) - (Higher awareness might lead to protective behavior/policy demand) Note: Limitations apply to this proxy. NDVI Normalized Difference Vegetation Index (proxy for vegetation density/health) NASA MODIS (MOD13Q1 V6) Index (-1 to 1) 2021 - (Vegetation might reduce dust/erosion) Imports Total import value (proxy for trade engagement/potential import of contaminated goods) International Trade Centre (ITC) Trade Map USD 2023 + (Imports, esp. e-waste, can be exposure source) Sanitation Share of population using at least basic sanitation services (proxy for infrastructure/public health capacity) WHO/UNICEF JMP (via Our World in Data) % 2021 - (Better infrastructure might correlate with lower exposure/better health systems) [Table 1 is about here] 3.1.1 Number of premature deaths from lead exposure The primary outcome variable, the number of premature deaths attributed to lead exposure (LED), represents the estimated number of deaths where lead exposure is a contributing risk factor, accounting for associated outcomes like cardiovascular and kidney diseases. This variable directly measures the health burden of lead exposure at a national level. The Institute for Health Metrics and Evaluation (IHME) estimates are widely used in global health research to quantify disease burden and attributable mortality, providing a standardized metric essential for cross-country comparisons of lead’s impact. Data was obtained from the IHME Global Burden of Disease (GBD) study via Our World in Data ( https://vizhub.healthdata.org/gbd-results/ ). IHME uses complex modeling incorporating exposure data, relative risks, and mortality rates, adjusting for age structures to yield a comparable, cross-national metric of mortality attributable to lead, essential for assessing the health impact of environmental exposures in low‐ and middle‐income settings. The primary limitations of this data are that GBD estimates for regions with sparse primary data, like parts of Africa, rely heavily on modeling and imputation, introducing uncertainty. 3.1.2 Economic indicators (national GDP) Gross Domestic Product (GDP) data was acquired from Natural Earth ( https://www.naturalearthdata.com/ ). GDP (estimates in millions USD) serves as a fundamental indicator of a nation’s level of economic development. While popular, using Aggregate GDP is admittedly a crude measure for national development, not capturing income distribution, economic structure, or governance quality, which might independently affect environmental health. Still, at the current research design, this crude measure suffices our initial understanding between national development and lead exposure caused premature death. 3.1.3 Normalized Difference Vegetation Index (NDVI) NDVI provides a standardized, remotely sensed measure of vegetation density and health. Dense vegetation can reduce airborne dust resuspension and act as a barrier to lead‑contaminated soil particulates, thereby mitigating exposure pathways, particularly in rapidly urbanizing areas where vegetation cover influences ambient lead dust levels (Frndak et al. 2022 ). NDVI data from NASA’s MODIS sensor (MOD13Q1 V6, 250m resolution, 16-day composites) was used as a proxy for national environmental conditions in this study. 3.1.4 Sanitation infrastructure Adequate sanitation systems not only reduce fecal-oral disease transmission but also serve as proxies for broader investment in water, waste, and environmental management, factors linked to lower exposure to soil and water pollutants, including lead (Selendy 2011 ). Data on the “Share of the population using at least basic sanitation services” from the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) via Our World in Data served as a proxy for infrastructure development and public health capacity ( https://ourworldindata.org/grapher/share-of-population-with-improved-sanitation-faciltities ). The JMP estimates are based on a combination of household surveys, censuses, and administrative data. 3.1.5 Lead paint regulation Legally binding controls on lead in paint directly target one of the most persistent and widespread sources of childhood and adult lead exposure (Dignam et al. 2019 ). Whether a country has adopted and enforces a lead-paint limit is a critical policy lever; evaluating its presence (binary) allows assessment of lead-related regulation’s role in reducing longterm exposure burdens. Data on legally-binding controls on lead paint for 2023 was sourced from the WHO Global Health Observatory ( https://www.who.int/data/gho/data/themes/topics/indicator-groups/legally-binding-controls-on-lead-paint ). The regulatory status information was transformed into a binary variable (Leadpaint) indicating lead paint ban as 1 and use of lead paint as 0. 3.1.6 Google Index for Lead Awareness In contexts where systematic survey data on awareness are lacking, Google Trends has been shown to capture population-level interest shifts, providing insight into when and where lead becomes a public concern (Fu et Miller 2022; Ginsberg et al. 2009 ). Based on a previous study in Nigeria (Fabolude et al. 2025 ), Google Trends relative search volume (RSV) for “lead exposure” and “lead poisoning” (averaged 2004–2021, weighted for internet penetration) are used in this study to proxy public awareness. In this study, we do realize some potential limitations of utilizing this proxy for lead public awareness, such as its only reflecting Internet active population’s awareness, possible language/categorization issues, and potential noises using standard normalization approach (Knoble et al. 2024 ; Knipe et al. 2021 ). We still employ this proxy because of the lacking of other systematically collected data. We hope this “big data” approach could provide some insight into the relative level of public information-seeking behavior related to lead hazards across African nations, as often employed when data is scarce (Thompson et al. 2022 ). 3.1.7 Imports data Total import values serve as an indicator of global trade engagement and potential inflow of lead‑containing products (e.g., e‑waste, leaded ceramics) (Chen et al. 2024 ). High import volumes may reflect pathways through which hazardous materials enter domestic markets, particularly where regulatory oversight is weak (Baggs 2009 ), giving rise to informal recycling activities and increased exposure. Total import values (USD) from the International Trade Centre (ITC) Trade Map ( https://www.trademap.org/Index.aspx ) were used as an indicator of trade engagement and potential pathways for lead-containing products (e.g., e-waste). We extracted total import values in dollars for each African country, representing the monetary value of all goods and materials imported annually. 3.2 Methodology Multivariate linear regression analysis was employed using R (version 4.5.0) to examine the relationship between premature deaths from lead exposure (LED) and the independent variables: Leadpaint, GDP, Googleindex, NDVI, Imports, and Sanitation. Prior to the multivariate regression, preliminary exploratory data analysis was conducted. The dependent variable, LED was found to be highly skewed to the left (large numbers of small values), a natural logarithmic transformation makes the dependent variable approximately normal. Scatterplots between the log-transformed LED and all independent variables also suggest that log-transformation of GDP and Imports show reasonably well linear relationships with log-transformed LED. For the other variables, the linear relationships are sufficient to build the model. Based on this preliminary exploration, a log-log specification of the model is built for final evaluation. Specifically, the model takes the form: $$\:log\left(LED\right)={\beta\:}_{0}+{\beta\:}_{1}\text{log}\left(GDP\right)+{\beta\:}_{2}Leadpaint+{\beta\:}_{3}Googleindex+{\beta\:}_{4}NDVI+{\beta\:}_{5}Imports+{\beta\:}_{6}sanitation+\epsilon\:$$ In addition to the multivariate regression model, we also examine potential interaction effects between the lead paint regulation status (the binary variable) and the GDP, Imports and awareness level (proxied by Google Trends). These interaction terms were included to explore whether the impact of economic development (GDP), import volumes, and public awareness (Google Index) on lead mortality differ systematically between countries that have a legally binding lead paint ban and those that do not. Furthermore, since we are using geographical information for regression analysis, it is customary to test the regression residuals for potential spatial autocorrelation (D.L. Yu et Wei 2008). Because our study is on the national level, and there is an island nation (Madagascar), to avoid overly imbalanced weight matrix structure or island effect, we designed a sphere of influence approach that is based on the Delaunay Triangulation to construct the spatial weights matrix, addressing potential issues with island/non-contiguous territories (D. Yu 2025a ). The residuals’ spatial autocorrelation is tested through Moran’s Index using the spdep package in R. If spatial autocorrelation is detected, the Lagrange Multiplier test is conducted to determine which specification (spatial lag or spatial error) would be more appropriate. Technical details are abundant and will not be repeated here. Interested scholars are encouraged to consult D. Yu ( 2025a ) for more details. 4. Results 4.1 Spatial Distribution of LED The spatial distribution of LED in 2021 showed substantial geographic variation (Fig. 1 ). North and Northeast Africa exhibited the highest burden, with Egypt reporting the highest estimated mortality (38,000 deaths). West Africa generally showed moderate to low rates, except for Nigeria (second highest, > 12,000 deaths). Morocco (10,500), Ethiopia (10,000), and Algeria (9,800) followed (Fig. 2 ). This distribution highlights lead exposure as a significant public health issue across diverse African regions. [Figures 1 and 2 are about here] 4.2 Regression analysis and diagnostics The multivariate linear regression model using the log-log specification has produced rather interesting results, which is reported in Table 2 . Multicollinearity is checked with variance of inflation (VIF) and also reported in Table 2 . The three interaction terms (Leadpaint with GDP, Imports, and Googleindex) are included one at a time and all at once (models 2–5). We expect that legally-binding lead paint bans will strengthen the influence of economic development, import volumes, and public awareness on reducing premature lead exposure mortality. However, results suggest that except for the interaction term of Leadpaint with Googleindex, the inclusion of other two interaction terms causes severe multicollinearity (Table 2 ), which substantially inflates the standard errors of the associated coefficients, rendering their individual estimates unstable and making it difficult to reliably interpret their distinct impacts on lead mortality. The inclusion of the interaction term between Leadpaint with Googleindex, on the other hand, did not improve the model sufficiently to warrant the specification based on the Akaike Information Criterion (Table 2 ). Table 2 Multivariate Regression Model results, VIF, and Spatial Diagnostics (1) Base OLS Base OLS VIF (2) GDP \(\:\times\:\) LP (3) Imports \(\:\times\:\) LP (4) Google \(\:\times\:\) LP (5) All 3 Interactions Intercept -0.290 0.948 0.229 -0.273 1.341 (0.878) (1.018) (1.022) (0.856) (0.953) log(GDP) 0.560*** 4.20 0.470** 0.553*** 0.516*** 0.303* (0.134) (0.135) (0.134) (0.133) (0.139) log(Imports) 0.379* 4.33 0.340* 0.323+ 0.449** 0.528** (0.153) (0.148) (0.163) (0.154) (0.157) Google Index -0.016+ 1.16 -0.014+ -0.015+ -0.009 -0.004 (0.008) (0.008) (0.008) (0.009) (0.008) NDVI -0.997 1.35 -0.977 -0.939 -1.129 -1.274* (0.695) (0.668) (0.698) (0.681) (0.625) Sanitation -0.023*** 1.41 -0.024*** -0.023*** -0.026*** -0.029*** (0.005) (0.005) (0.005) (0.005) (0.005) Leadpaint (LP) -0.057 1.19 -3.353* -1.408 0.392 -3.882** (0.271) (1.539) (1.387) (0.360) (1.430) GDP \(\:\times\:\) LP 0.305* (0.140) 0.762* (0.283) Imports \(\:\times\:\) LP 0.155 (0.156) -0.443 (0.313) Google Index \(\:\times\:\) LP -0.036+ (0.020) -0.044* (0.019) Num. Obs. 52 52 52 52 52 VIF: LP 1.19 41.39 31.03 2.20 42.02 VIF: GDP \(\:\times\:\) LP 45.60 218.77 VIF: Imports \(\:\times\:\) LP 34.46 176.11 VIF: Google Index \(\:\times\:\) LP 2.55 2.97 AIC 129.86 126.56 130.71 128.01 119.70 Moran’s I (Residuals of Base OLS): 0.259, p-value = 0.002 LM Error (Rao Score): 6.841 (df = 1), p-value = 0.009 LM Lag (Rao Score): 6.515 (df = 1), p-value = 0.011 Robust LM Error (Rao Score): 1.758 (df = 1), p-value = 0.185 Robust LM Lag (Rao Score): 1.432 (df = 1), p-value = 0.231 Significance symbol: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 In addition, the Moran’s Index for the model’s residual show significant spatial autocorrelation (Moran’s I = 0.259, p-value = 0.002). The Lagrange Multiplier test is then conducted, and the Rao’s Scores for both spatial error and spatial lag models are also reported in Table 2 . The test suggests tha a spatial error model (SEM) provides a more appropriate alternative, suggesting that there are missing variables that are significantly spatially autocorrelated. The results of the SEM are reported in Table 3 . Table 3 Summary of Spatial Error autoregressive model Estimate Std. Error z value Pr(>|z|) (Intercept) -0.383 0.726 -0.528 0.597 log(GDP) 0.557*** 0.116 4.783 0.000 log(Imports) 0.342** 0.129 2.655 0.008 Lead paint -0.018 0.207 -0.085 0.932 Google Index -0.014+ 0.007 -1.904 0.057 NDVI -0.370 0.768 -0.482 0.630 Sanitation -0.019*** 0.005 -4.044 0.000 Lambda (Spatial Error Parameter) Estimate: 0.50322*** Asymptotic standard error: 0.136 z-value: 3.712, p-value: 0.0002 LR test (vs OLS): 7.7534, p-value: 0.0053612 Wald statistic: 13.7805, p-value: 0.0002055 Log likelihood: -53.05513 for error model Number of observations: 52 AIC: 124.11, (AIC for lm: 129.86) Significance symbol: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 [Tables 2 and 3 are about here] 5. Discussion The results presented Table 3 paint an interesting and farily revealing story about economic development, international trade, public infrastructure, and awareness-related factors in shaping lead exposure caused mortality. In the following subsections, we interpret these results in detail and discuss relevant policy implications. 5.1. Advancing the Analysis: The Imperative of a Spatial Econometric Approach Our analysis began with an Ordinary Least Squares (OLS) regression as a baseline (Table 2 , Model 1). After examining the interaction terms and deciding that the inclusion is not well justified (Table 2 , models 2–5), we conducted diagnostic tests on the OLS residuals. The test revealed a Moran’s I of 0.259 (p = 0.002), indicating significant positive spatial autocorrelation in the residuals, which is commonly the case when geographically referenced data is used for analysis. Lagrange Multiplier (LM) tests reinforced the need for a spatial error model (Rao’s score for error model = 6.84, p = 0.009). Although robust LM tests were less conclusive (p > 0.1), the strong Moran’s I and standard LM results motivated the use of an SEM (D. Yu 2025a ). This suggests that there are other spatially autocorrelated but unobserved factors contributing to the variance of premature mortality attributable to lead exposure. Not surprisingly, the spatial error coefficient ( \(\:\lambda\:\) ) is ~ 0.503 and significant (p < 0.001), confirming that a portion of variance in national lead mortality is explained by unobserved factors that are spatially correlated. The baseline model did not include those pieces of information, either due to lack of data or lack of systematic monitoring. The SEM provides a better fit (AIC = 124.11) than the non-spatial OLS model (AIC = 129.86), suggesting that accounting for spatial dependency improves explanatory power. This result suggests that, in essence, lead poisoning risks are not independent country by country; there are region-specific influences that affect mortality. These could include regional industry patterns (for example, clusters of mining activity in West Africa or Southern Africa), cross-border movement of leaded products (such as regional trade in batteries or spices), or shared historical legacies (e.g., neighboring countries having similar regulations or fuel use histories). The presence of spatially correlated errors means that purely national-level analyses could misattribute some effects. Some countries’ high lead burdens may be driven by very specific sources – for instance, Zambia’s Copperbelt (Kabwe) with extensive lead mine tailings, or the infamous lead poisoning outbreak in Zamfara, Nigeria from artisanal gold mining (Fabolude et al. 2025 ). Such localized crises would not be fully captured by national averages. Other potential factors include the scale of the informal economy (e.g. how prevalent backyard battery recycling or cottage industries are), urban population distribution (intensity of slum dwelling), general environmental governance and corruption levels, nutritional factors (populations with calcium/iron deficiency absorb more lead), and so on. Data on many of these aspects are scarce or difficult to quantify across all countries. Indeed, a major limitation in studying lead in Africa is the paucity of standardized data on environmental lead concentrations and BLLs. Few African nations conduct regular biomonitoring for lead; most available data come from isolated research studies or global modeled estimates (Fabolude et al. 2025 ; Lo et al. 2012 ). This lack of direct data likely contributes to uncertainty and may cause some relationships to go undetected. Our use of proxies (like Google searches or NDVI) was in part to innovatively compensate for data gaps. Despite the lack of data, our spatial regression approach is able to provide a useful macro-level picture: it highlights broad patterns and flags where policy attention is needed, even as more granular research continues. 5.2. The Double-Edged Sword: Economic Development, Trade, and Lead Mortality A striking finding is the positive and significant association between economic growth and lead exposure mortality. The coefficient for log(GDP) in the SEM is 0.557 (p < 0.001), indicating that, on average, higher national income is associated with higher lead-attributable death rates (Table 3 ). This counterintuitive result presents a “development paradox,” whereby economic advancement correlates with worse environmental health outcomes in the context of lead (Adu Sarfo et Tweneboah 2024). Such paradox agrees with the Environmental Kuznets Curve (EKC) theory that many African countries are experiencing the initial stage of industrialization/urbanization where pollution intensifies during early industrialization and growth before improving at later stages of development (Kupzig, Kupzig, et Meier 2024). In these early phases, rapid GDP gains often stem from expansion in manufacturing, construction, mining, and other industrial activities that, without stringent safeguards, increase environmental lead contamination. For example, resource extraction and processing (such as lead mining or artisanal gold mining) can release lead into the environment, as seen in Nigeria’s mining regions (Fabolude et al. 2025 ). Likewise, urbanization accompanying GDP growth may involve unregulated industrial emissions and proliferation of leaded consumer products, contributing to higher population exposure. This alignment of our findings with the EKC theory has been observed in other contexts: rising incomes in low-to-middle income settings can correlate with increased pollution until regulatory and technological investments eventually catch up (Kordas et al. 2018 ). Our results suggest that in Africa, economic growth to date has been predominantly pollution-intensive – outpacing the implementation of environmental health protections. The implication is that simply growing the economy, without deliberate public health safeguards, may exacerbate lead exposure risks rather than alleviate them. This underscores the need to integrate environmental health measures early in the development process, rather than assuming improvements will automatically materialize at higher income levels. International trade emerges as another double-edged sword. log(Imports) is also positively associated with lead mortality (coefficient 0.342, p = 0.008 in the SEM), implying that countries with greater import volumes tend to suffer higher lead-related premature deaths. This finding supports the notion that globalization can facilitate toxic exposure pathways (Lebbie et al. 2021; Chen et al. 2024 ). One mechanism is through the import of lead-containing products and wastes. African countries often receive used or low-cost products that contain lead – for instance, electronics, batteries, cosmetics, paints, toys, and ceramics with lead components (Chen et al. 2024 ). A considerable portion of the world’s electronic waste (e-waste) is shipped to parts of Africa under the guise of second-hand goods or recycling, but in reality, much of it is unsafely processed in informal sectors. This informal e-waste recycling – exemplified by sites like Agbogbloshie in Accra, Ghana – releases lead and other toxic metals into surrounding communities (Lebbie et al. 2021; Püschel et al. 2024 ). Mismanaged e-waste and used lead-acid batteries are identified as “one of the most alarming sources of lead poisoning” in Africa, as informal recycling releases lead particles into soil and air (Muhavani et Mangwiro 2025). The positive import effect in our model resonates with the Pollution Haven Hypothesis (Copeland 2008 ), which suggests that hazardous materials and polluting industries may flow toward regions with less stringent regulations. African nations with weaker enforcement can become dumping grounds for lead-laden waste and products, inadvertently importing environmental health risks along with goods. Notably, both GDP (a proxy for domestic economic activity) and Imports (a proxy for trade exposure) remain significant in the spatial model, indicating that multiple economic avenues – internal growth and global trade – are contributing to the continent’s lead burden. Economic integration into global markets clearly brings benefits, but without adequate oversight on environmental regulations, it also amplifies the influx of lead hazards, from old car batteries to contaminated consumer goods. This suggests that policies must address not only domestic industrial pollution but also the transboundary movement of toxic materials. 5.3. Public Infrastructure and Awareness: Protective Factors and Data Limitations Our model suggests that enhanced sanitation infrastructure (sanitation = − 0.019, p < 0.001) is an important factor for reducing lead exposure mortality. Countries with better public sanitation (a higher share of the population with basic sanitation services) experience fewer deaths from lead exposure, all else being equal. Improved sanitation can be viewed as a proxy for broader investments in public health infrastructure and environmental management. Nations that are able to provide clean water, waste disposal, and hygienic living conditions likely also have better systems for managing environmental pollutants, including hazardous waste and contaminated sites. For example, proper sanitation and waste management can prevent lead-contaminated refuse or sewage from polluting communities, and it often correlates with effective governance that could extend to controlling industrial pollution. The significant impact of sanitation in our model aligns with the idea that fundamental public health infrastructure reduces exposure pathways – a finding consistent with literature linking improved water/sanitation services to lower pollutant burdens (Wolf et al. 2023). It is notable that sanitation remains a strong predictor even when accounting for GDP and imports. This implies that how nations invest their wealth (e.g. in public health infrastructure) can offset some detrimental effects of industrialization. In policy terms, bolstering basic environmental health infrastructure may be a tangible near-term strategy to mitigate lead exposure, independent of a country’s income level. Public awareness of lead hazards, proxied by the Google search index, exhibited a negative coefficient as expected (higher awareness potentially associated with lower mortality), but this relationship was only marginally significant (p ≈ 0.057) in the SEM. While only marginally significant, we consider this particular result one critical contribution our current study made towards studying lead exposure risks in the Global South. Data scarcity often presents as one of the primary obstacles when conducting environmental health studies in the Global South. Not because the countries do not want to monitor and record environmental health status, but because they simply lack the means to do so. Internet search, while still biased by potential digital divide (Cariolle 2021 ; Jafar et al. 2024 ) in the Global South, provides a viable alternative to proxy such an important factor in fighting against lead exposure risks. The negative sign found in our SEM is encouraging and aligns with the hypothesis that a more informed public will take actions to reduce exposure (for instance, avoiding known sources or pressuring governments for action). The index we used (search volume for “lead exposure/poisoning”) was adjusted for internet penetration, but we do admit that it still mainly represents the connected, literate segment of society. It may therefore underestimate awareness levels in rural or low-income communities that rely on traditional knowledge channels. Moreover, even if people are aware of lead dangers, they may lack the means to act on that knowledge in the absence of institutional support. This weaker statistical significance might even suggest a synergy: public awareness can only go so far unless accompanied by a framework that enables protective action. Still, the inclusion of this proxy create an opportunity for studies in the near future to take advantage of the power brought by the “big data era” (Mayer-Schönberger et Cukier 2013). 5.4. Regulatory Measures and Environmental Context Somewhat surprisingly, the lead paint regulation variable was not statistically significant in our model (–0.018, p = 0.93), suggesting no measurable difference in current lead mortality between countries that have instituted lead paint laws and those that have not. This null finding should be interpreted with caution. First, our binary indicator is a crude measure – it does not capture the strength or enforcement of the regulation, nor how long it has been in effect. Many African nations only recently enacted lead paint limits (often 90 ppm total lead, as recommended globally), and enforcement may lag behind legislation (Muhavani et Mangwiro 2025). It can take years for regulations to translate into public health improvements, especially for a pollutant like lead that persists in the environment. Additionally, countries that adopted regulations might have done so in response to severe lead problems, meaning the policy’s presence could be an indicator of high underlying exposure (a form of reverse causality). In short, the lack of a clear statistical benefit from lead paint laws in the data does not mean such regulations are ineffective; rather, it highlights that simply having a law on the books might not necessarily be sufficient. Effective implementation (industry compliance, public awareness of the law, and active enforcement) is critical for these policies to realize their life-saving potential. The NDVI, a proxy for national environmental conditions, also showed no significant association with lead mortality in our model (coefficient ≈ − 0.37, p = 0.63). This result indicates that, at a national scale, greenness is probably not a consistent predictor of lead exposure risks. One possible explanation is that the major sources of lethal lead exposure in Africa are largely anthropogenic and localized (industry, mines, batteries, paints, etc.), so broad differences in environmental conditions do not translate into differences in lead exposure caused mortality. While vegetation can reduce airborne dust in specific locales, this effect may be too geographically limited to influence country-level mortality rates. Moreover, in urban slums or industrial hotspots with extreme lead contamination, even high NDVI (parks, trees) might not prevent exposure if, say, children are playing in highly contaminated soils or dust. Conversely, some very arid areas with low NDVI might have little lead pollution if they lack industry (the Sahara has lots of dust but low industrial lead sources). These factors in Africa suggest that NDVI was likely overshadowed by more direct drivers of exposure. We interpret the null result not as evidence that the environment does not matter (vegetation certainly has benefits), but that lead pollution in Africa is driven more by human factors than by natural landscape features. 5.5. Policy Implications Addressing the “lead exposure paradox” in Africa requires an integrated approach that meshes economic development with proactive environmental health safeguards. Our findings make clear that without intervention, economic growth and globalization can continue to drive up lead exposure – an outcome that is neither inevitable nor acceptable. The persistence of lead hazards is fundamentally a market failure – a classic case of socializing costs, privatizing gains: those responsible for pollution (whether industries or traders) often do not bear the full costs, while the health and economic damages fall on the public (externalities), especially on vulnerable groups like children. Correcting this market failure will require a combination of regulatory enforcement and inventive policy tools. Traditional command-and-control regulations – for example, banning leaded paint, mandating safer battery recycling practices, tightening standards on imports of used electronics – are essential. These set the rules of the game. Yet, laws on paper must be matched by enforcement capacity and public compliance. Market-based instruments can complement these efforts by shifting incentives: environmental taxes or fees can internalize the cost of lead pollution, and subsidies or loans can encourage businesses to adopt lead-free alternatives and recycling technologies. For instance, a tax on used battery imports could fund proper recycling facilities, or micro-credit could help informal recyclers transition to safer jobs. Innovative financing, including green bonds or international aid targeted at pollution control, could support the up-front costs of cleanup and prevention. Tackling the informal sector is particularly challenging but crucial. In Africa, a large share of lead pollution stems from informal operations – whether it is backyard smelting of batteries, open-air burning of e-waste, or cottage industries using leaded glazes. Simply outlawing these practices without providing alternatives can backfire, as people depend on them for livelihoods. A more pragmatic strategy is to formalize and incentivize safer practices. This might include establishing collection programs where used batteries can be exchanged for a small payment (to ensure they go to licensed recyclers), offering training and equipment for safer e-waste disassembly, or creating “alternative livelihood” programs in mining areas so that small-scale miners are not forced to resort to high-risk activities. Such approaches, coupled with remediation of contaminated sites, can gradually reduce legacy lead exposure while sustaining economic well-being. In Africa, the economic burden of lead exposure is estimated at $ 134.7 billion per year (about 4% of regional GDP) due to lost lifetime productivity and healthcare expenses (Attina et Trasande 2013). These staggering losses far outweigh the costs of intervention, indicating that lead pollution is not just a health issue but also a significant drag on development. To reverse the trend of rising lead harm amid development, African governments and international partners need to prioritize environmental health governance. This means updating and enforcing regulations (e.g. ensuring the new 90 ppm paint standards are adhered to), strengthening institutions that monitor pollution and health (establishing surveillance of blood lead levels, tracking imports of toxic substances), and fostering regional cooperation (Fabolude et al. 2025 ). Many lead issues are transboundary. for example, contaminated consumer goods can easily cross borders. Regional blocs like ECOWAS and the African Union have a role to play in harmonizing standards and stopping the shunting of toxic products from one country to another. Public awareness campaigns should be ramped up, leveraging both traditional media and community outreach, since informed communities can demand change and protect themselves better (especially when they are given the tools and legal backing to do so). The recent global phase-out of leaded gasoline – achieved in 2021 after coordinated effort – shows that large-scale elimination of a lead source is possible with political will and public pressure. A similar commitment is now needed for the next generation lead threats such as lead in paints, batteries, and e-waste. Our study underscores a critical insight for African or the entire Global South’s sustainable development: economic growth and public health must advance hand in hand. The trade-offs observed between GDP growth and lead mortality are not a reason to stifle development, but a call to steer development onto a sustainable path where increasing prosperity does not come at the cost of poisoning the population. By investing in infrastructure, enforcing safeguards, and innovating policy solutions, Africa can rise out of the development paradox and break the link between development and pollution – ensuring that the pursuit of prosperity shields rather than sacrifices the health of its people. Such proactive measures are not only morally imperative but economically sound, paving the way for a healthier, more productive future across the continent. 6. Conclusion This study investigated the interplay between socioeconomic, environmental, and policy factors and premature mortality attributed to lead exposure across African nations. A key finding is the concerning “development paradox,” whereby higher national GDP is positively associated with increased lead-related deaths. This paradox underscores a critical gap: economic expansion across Africa frequently outpaces the development and enforcement of necessary environmental health protections. International trade emerges as another influential factor, with greater import volumes, particularly of hazardous materials like e-waste, exacerbating exposure risks. Though intertwined with GDP effects, trade remains a distinct pathway for environmental lead contamination. Interestingly, interventions like lead paint regulations did not exhibit immediate significant associations with reduced mortality, potentially reflecting implementation challenges, regulatory enforcement issues, and the persistent nature of lead in the environment. The study also introduced a novel, big data-driven proxy for public awareness, Google Trends search volumes, which showed marginal significance. This modest association suggests public awareness alone might be insufficient without enabling regulatory frameworks. The limitations of using internet-based metrics due to Africa’s digital divide further highlight the urgent need for innovative and inclusive data collection strategies. This pioneer study on integrating big data analytics, remote sensing, spatial data analysis, and environmental health concerns across the African continent is promising to understand lead related premature mortality in developing regions. Still, it is important to acknowledge potential limitations inherent to our ecological study design. At the current stage, the use of aggregated national-level data increases the potential for ecological fallacy, in which observed associations at the country level may not accurately represent individual-level relationships. Unfortunately, the unavailability of data and field work prevents further exploration that we aim for next phase of studies. Additionally, while the spatial error specification is able to provide from a spatial analysis perspective the influence of missed, but spatially autocorrelated factors, residual confounding likely persists such as detailed individual-level socioeconomic status, localized environmental conditions, and the precise extent of enforcement for existing regulations. These limitations should prompt cautious interpretation of our findings and underscore the need for complementary individual-level studies or sub-national analyses to validate and refine these relationships. Addressing Africa’s lead burden effectively requires more than incremental adjustments; it demands a paradigm shift integrating robust environmental health safeguards directly into the core of economic policies. Future strategies must emphasize proactive, rigorously enforced regulations, complemented by innovative market-based instruments. Equally essential is supporting and incentivizing safer practices within Africa’s substantial informal sector, as outright bans without alternatives may exacerbate economic hardships. The remarkable economic returns from lead poisoning prevention, estimated at $ 17 to $ 220 for every dollar invested, strongly justify prioritizing these preventive measures. Achieving significant reductions in lead-related harm will require multi-sectoral cooperation, diverse funding strategies, strengthened governance frameworks, and regional collaboration. In addition, substantial improvements in environmental surveillance and data collection, integrating traditional biomonitoring with emerging big data and remote-sensing technologies, are needed to fill current knowledge gaps. Further research using longitudinal designs, refined measures of regulatory effectiveness and awareness, and granular sub-national analyses will enhance our understanding and inform targeted, effective interventions. Ultimately, decoupling economic development from environmental degradation through comprehensive, integrated strategies is paramount to safeguarding the health and future prosperity of African populations. Declarations Funding acknowledgement: The work that provided the basis for this research was supported by funding under an award with the U.S. Department of Housing and Urban Development (grant number NJLTS0027-22). The substance and findings of the work are dedicated to the public. The author and publisher are solely responsible for the accuracy of the statements and interpretations contained in this publication. Such interpretations do not necessarily reflect the views of the Government. Author Contribution G.F. and D.Y. wrote the main manuscript text. G.F. and I.A. prepared the data. G.F., C.K. and A.V. compiled the data for final analysis. G.F. and D.Y. conducted the data analyses. D.Y. preapred Figure 1. G.F. prepared Figure 2. G.F., C.K., A.V., and D.Y. prepared Table 1. D.Y. prepared Tables 2 and 3. All authors reviewed the manuscript. Data Availability Data will be available upone request from the corresponding author. References Abogunrin-Olafisoye, Oladunni B., et Oladayo Adeyi. 2025. « Environmental and health impacts of unsustainable waste electrical and electronic equipment recycling practices in Nigeria's informal sector. » Discover Chemistry 2 (1). https://doi.org/10.1007/s44371-024-00075-x. https://dx.doi.org/10.1007/s44371-024-00075-x. Adu Sarfo, Emmanuel, et Rabbi Tweneboah. 2024. « Mineral wealth paradox: health challenges and environmental risks in African resource-rich areas. » BMC Public Health 24 (1): 724. https://doi.org/10.1186/s12889-024-18137-1. https://doi.org/10.1186/s12889-024-18137-1. Attina, Teresa M., et Leonardo Trasande. 2013. « Economic Costs of Childhood Lead Exposure in Low- and Middle-Income Countries. » Environmental Health Perspectives 121 (9): 1097-102. https://doi.org/doi:10.1289/ehp.1206424. https://ehp.niehs.nih.gov/doi/abs/10.1289/ehp.1206424. Baggs, Jen. 2009. « International Trade in Hazardous Waste. » Review of International Economics 17 (1): 1-16. https://doi.org/https://doi.org/10.1111/j.1467-9396.2008.00778.x. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9396.2008.00778.x. Bede-Ojimadu, Onyinyechi, Cecilia Nwadiuto Amadi, et Orish Ebere Orisakwe. 2018. « Blood Lead Levels in Women of Child-Bearing Age in Sub-Saharan Africa: A Systematic Review. » Frontiers in Public Health 6. https://doi.org/10.3389/fpubh.2018.00367. https://dx.doi.org/10.3389/fpubh.2018.00367. Brunnschweiler, Christa N., Deanna Karapetyan, et Päivi Lujala. 2024. « Opportunities and risks of small-scale and artisanal gold mining for local communities: Survey evidence from Ghana. » The Extractive Industries and Society 17: 101403. https://doi.org/10.1016/j.exis.2024.101403. https://dx.doi.org/10.1016/j.exis.2024.101403. Cariolle, Joël. 2021. « International connectivity and the digital divide in Sub-Saharan Africa. » Information Economics and Policy 55: 100901. https://doi.org/https://doi.org/10.1016/j.infoecopol.2020.100901. https://www.sciencedirect.com/science/article/pii/S0167624520301451. Chen, Wei, Jianing Zhang, Zhaoyuan Yu, et Xiquan Zhao. 2024. « Structure and evolution of global lead trade network: An industrial chain perspective. » Resources Policy 90: 104735. https://doi.org/https://doi.org/10.1016/j.resourpol.2024.104735. https://www.sciencedirect.com/science/article/pii/S0301420724001028. Copeland, Brian R. 2008. « The pollution haven hypothesis. » Handbook on Trade and the Environment 2 (7): 60-70. Desye, Belay, Amensisa Hailu Tesfaye, Gete Berihun, Ayechew Ademas, et Birhanu Sewunet. 2023. « A systematic review of the health effects of lead exposure from electronic waste in children. » Frontiers in Public Health 11. https://doi.org/10.3389/fpubh.2023.1113561. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2023.1113561. Dignam, Timothy, Rachel B. Kaufmann, Lauren Lestourgeon, et Mary Jean Brown. 2019. « Control of Lead Sources in the United States, 1970-2017: Public Health Progress and Current Challenges to Eliminating Lead Exposure. » Journal of Public Health Management and Practice 25 (1): S13-S22. https://doi.org/10.1097/phh.0000000000000889. https://dx.doi.org/10.1097/PHH.0000000000000889. Dórea, José G. 2019. « Environmental exposure to low-level lead (Pb) co-occurring with other neurotoxicants in early life and neurodevelopment of children. » Environmental Research 177: 108641. https://doi.org/10.1016/j.envres.2019.108641. https://dx.doi.org/10.1016/j.envres.2019.108641. Fabolude, Gift, Charles Knoble, Anvy Vu, et Danlin Yu. 2025. « Beyond mining: A pioneer attempt to assessing lead exposure risks in Nigeria. » Environmental Impact Assessment Review 114: 107923. https://doi.org/https://doi.org/10.1016/j.eiar.2025.107923. https://www.sciencedirect.com/science/article/pii/S0195925525001209. Forsyth, J. E., K. L. Weaver, K. Maher, M. S. Islam, R. Raqib, M. Rahman, S. Fendorf, et S. P. Luby. 2019. « Sources of Blood Lead Exposure in Rural Bangladesh. » Environ Sci Technol 53 (19): 11429-36. https://doi.org/10.1021/acs.est.9b00744. Frndak, Seth, Gabriel Barg, Elena I. Queirolo, Nelly Mañay, Craig Colder, Guan Yu, Zia Ahmed, et Katarzyna Kordas. 2022. « Do Neighborhood Factors Modify the Effects of Lead Exposure on Child Behavior? » Toxics 10 (9): 517. https://www.mdpi.com/2305-6304/10/9/517. Fu, C., et C. Miller. 2022. « Using Google Trends as a proxy for occupant behavior to predict building energy consumption. » Applied Energy 310: 118343-43. https://doi.org/10.1016/J.APENERGY.2021.118343. Ginsberg, Jeremy, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski, et Larry Brilliant. 2009. « Detecting influenza epidemics using search engine query data. » Nature 457 (7232): 1012-14. https://doi.org/10.1038/nature07634. https://doi.org/10.1038/nature07634. Gould, E. 2009. « Childhood lead poisoning: conservative estimates of the social and economic benefits of lead hazard control. » Environ Health Perspect 117 (7): 1162-7. https://doi.org/10.1289/ehp.0800408. Jafar, Zain, Jonathan D. Quick, Eszter Rimányi, et Godfrey Musuka. 2024. « Social Media and Digital Inequity: Reducing Health Inequities by Closing the Digital Divide. » International Journal of Environmental Research and Public Health 21 (11): 1420. https://www.mdpi.com/1660-4601/21/11/1420. Knipe, Duleeka, David Gunnell, Hannah Evans, Ann John, et Daisy Fancourt. 2021. « Is Google Trends a useful tool for tracking mental and social distress during a public health emergency? A time–series analysis. » Journal of Affective Disorders 294: 737-44. https://doi.org/10.1016/j.jad.2021.06.086. https://linkinghub.elsevier.com/retrieve/pii/S0165032721006741. Knoble, Charles, Gift Fabolude, Anvy Vu, et Danlin Yu. 2024. « From crisis to prevention: mining big data for public health insights during the flint water crisis. » Discover Sustainability 5 (1): 289. https://doi.org/10.1007/s43621-024-00514-w. https://doi.org/10.1007/s43621-024-00514-w. Kordas, Katarzyna, Julia Ravenscroft, Ying Cao, et Elena V. McLean. 2018. « Lead Exposure in Low and Middle-Income Countries: Perspectives and Lessons on Patterns, Injustices, Economics, and Politics. » International Journal of Environmental Research and Public Health 15 (11): 2351. https://doi.org/10.3390/ijerph15112351. https://dx.doi.org/10.3390/ijerph15112351. Kupzig, Nina, Jenny Kupzig, et Johanna Meier. 2024. « An augmented Environmental Kuznets Curve and a new measure of water pollution: an investigation of water pollution in Africa. » Discover Water 4 (1): 122. https://doi.org/10.1007/s43832-024-00177-8. https://doi.org/10.1007/s43832-024-00177-8. Lanphear, B. P., R. Hornung, J. Khoury, K. Yolton, P. Baghurst, D. C. Bellinger, R. L. Canfield, K. N. Dietrich, R. Bornschein, T. Greene, S. J. Rothenberg, H. L. Needleman, L. Schnaas, G. Wasserman, J. Graziano, et R. Roberts. 2005. « Low-level environmental lead exposure and children's intellectual function: an international pooled analysis. » Environ Health Perspect 113 (7): 894-9. https://doi.org/10.1289/ehp.7688. Lebbie, Tamba S., Omosehin D. Moyebi, Kwadwo Ansong Asante, Julius Fobil, Marie Noel Brune-Drisse, William A. Suk, Peter D. Sly, Julia Gorman, et David O. Carpenter. 2021. « E-Waste in Africa: A Serious Threat to the Health of Children. » International Journal of Environmental Research and Public Health 18 (16): 8488. https://doi.org/10.3390/ijerph18168488. https://dx.doi.org/10.3390/ijerph18168488. Levin, Ronnie. 2016. « The attributable annual health costs of U.S. occupational lead poisoning. » International Journal of Occupational and Environmental Health 22 (2): 107-20. https://doi.org/10.1080/10773525.2016.1173945. https://doi.org/10.1080/10773525.2016.1173945. Lo, Yi-Chun, Carrie A. Dooyema, Antonio Neri, James Durant, Taran Jefferies, Andrew Medina-Marino, Lori De Ravello, Douglas Thoroughman, Lora Davis, Raymond S. Dankoli, Matthias Y. Samson, Luka M. Ibrahim, Ossai Okechukwu, Nasir T. Umar-Tsafe, Alhassan H. Dama, et Mary Jean Brown. 2012. « Childhood Lead Poisoning Associated with Gold Ore Processing: a Village-Level Investigation—Zamfara State, Nigeria, October–November 2010. » Environmental Health Perspectives 120 (10): 1450-55. https://doi.org/10.1289/ehp.1104793. https://dx.doi.org/10.1289/ehp.1104793. Mathee, A. 2014. « Towards the prevention of lead exposure in South Africa: contemporary and emerging challenges. » Neurotoxicology 45: 220-3. https://doi.org/10.1016/j.neuro.2014.07.007. Mathee, A., N. Naicker, T. Kootbodien, T. Mahuma, P. Nkomo, I. Naik, et T. de Wet. 2014. « A cross-sectional analytical study of geophagia practices and blood metal concentrations in pregnant women in Johannesburg, South Africa. » S Afr Med J 104 (8): 568-73. https://doi.org/10.7196/samj.7466. Mathee, Angela, Halina Röllin, Yasmin Von Schirnding, Jonathan Levin, et Ina Naik. 2006. « Reductions in blood lead levels among school children following the introduction of unleaded petrol in South Africa. » Environmental Research 100 (3): 319-22. https://doi.org/10.1016/j.envres.2005.08.001. https://dx.doi.org/10.1016/j.envres.2005.08.001. Mayer-Schönberger, Viktor, et Kenneth Cukier. 2013. Big data: A revolution that will transform how we live, work, and think . Houghton Mifflin Harcourt. Moya-Alvarez, Violeta, Michael Osei Mireku, Pierre Ayotte, Michel Cot, et Florence Bodeau-Livinec. 2016. « Elevated Blood Lead Levels Are Associated with Reduced Risk of Malaria in Beninese Infants. » PLOS ONE 11 (2): e0149049. https://doi.org/10.1371/journal.pone.0149049. https://dx.doi.org/10.1371/journal.pone.0149049. Muhavani, Angélique Umutesi, et Alexander Mangwiro. 2025. « Africa: Lead Poisoning - the Persistent Threat From Mismanaged Waste. ». United Nations Environment Programme (UNEP). Consulté le 5/14/2025. https://allafrica.com/stories/202501130438.html. Nriagu, Jerome O., Mary L. Blankson, et Kwamena Ocran. 1996. « Childhood lead poisoning in Africa: a growing public health problem. » Science of The Total Environment 181 (2): 93-100. https://doi.org/10.1016/0048-9697(95)04954-1. https://dx.doi.org/10.1016/0048-9697(95)04954-1. Ondayo, Maureene Auma, Michael J. Watts, Clive J. Mitchell, David C. P. King, et Odipo Osano. 2024. « Review: Artisanal Gold Mining in Africa—Environmental Pollution and Human Health Implications. » Exposure and Health 16 (4): 1067-95. https://doi.org/10.1007/s12403-023-00611-7. https://dx.doi.org/10.1007/s12403-023-00611-7. Oulhote, Youssef. 2015. « Lead Exposure In African Countries: State Of The Art, Challenges And Perspectives. » ISEE Conference Abstracts 2015 (1): 2173. https://doi.org/10.1289/isee.2015.2015-2173. https://dx.doi.org/10.1289/isee.2015.2015-2173. Pascale, Antonio, Adriana Sosa, Cristina Bares, Alejandra Battocletti, María José Moll, Darío Pose, Amalia Laborde, Hugo González, et Gabriella Feola. 2016. « E-Waste Informal Recycling: An Emerging Source of Lead Exposure in South America. » Annals of Global Health 82 (1): 197. https://doi.org/10.1016/j.aogh.2016.01.016. https://dx.doi.org/10.1016/j.aogh.2016.01.016. Pennington, Audrey F., Madison R. Smith, Stella O. Chuke, Cheryl R. Cornwell, Paul B. Allwood, et Joseph G. Courtney. 2024. « Effects of Blood Lead Levels <10 µg/dL in School-Age Children and Adolescents: A Scoping Review. » Pediatrics 154 (Supplement 2). https://doi.org/10.1542/peds.2024-067808F. https://doi.org/10.1542/peds.2024-067808F. Poudel, Kritika, Atsuko Ikeda, Hisanori Fukunaga, Marie-Noel Brune Drisse, Lesley Jayne Onyon, Julia Gorman, Amalia Laborde, et Reiko Kishi. 2024. « How does formal and informal industry contribute to lead exposure? A narrative review from Vietnam, Uruguay, and Malaysia. » Reviews on Environmental Health 39 (2): 371-88. https://doi.org/10.1515/reveh-2022-0224. https://dx.doi.org/10.1515/reveh-2022-0224. Püschel, P., K. M. Agbeko, A. A. Amoabeng-Nti, J. Arko-Mensah, J. Bertram, J. N. Fobil, S. Waldschmidt, K. Löhndorf, T. Schettgen, M. Lakemeyer, A. Morrison, et T. Küpper. 2024. « Lead exposure by E-waste disposal and recycling in Agbogbloshie, Ghana. » International Journal of Hygiene and Environmental Health 259: 114375. https://doi.org/10.1016/j.ijheh.2024.114375. https://dx.doi.org/10.1016/j.ijheh.2024.114375. SAMRC. 2019. « SAMRC supports global ban on lead in paint. ». https://www.samrc.ac.za/press-releases/samrc-supports-global-ban-lead-paint. Selendy, J.M.H. 2011. Water and Sanitation-Related Diseases and the Environment: Challenges, Interventions, and Preventive Measures . Wiley. Suk, William A., Hamid Ahanchian, Kwadwo Ansong Asante, David O. Carpenter, Fernando Diaz-Barriga, Eun-Hee Ha, Xia Huo, Malcolm King, Mathuros Ruchirawat, Emerson R. Da Silva, Leith Sly, Peter D. Sly, Renato T. Stein, Martin Van Den Berg, Heather Zar, et Philip J. Landrigan. 2016. « Environmental Pollution: An Under-recognized Threat to Children’s Health, Especially in Low- and Middle-Income Countries. » Environmental Health Perspectives 124 (3). https://doi.org/10.1289/ehp.1510517. https://dx.doi.org/10.1289/ehp.1510517. Thompson, Joshua J., Robert L. Wilby, Tom Matthews, et Conor Murphy. 2022. « The utility of Google Trends as a tool for evaluating flooding in data‐scarce places. » Area 54 (2): 203-12. https://doi.org/10.1111/area.12719. https://dx.doi.org/10.1111/area.12719. Tirima, Simba, Casey Bartrem, Ian Von Lindern, Margrit Von Braun, Douglas Lind, Shehu Mohammed Anka, et Aishat Abdullahi. 2016. « Environmental Remediation to Address Childhood Lead Poisoning Epidemic due to Artisanal Gold Mining in Zamfara, Nigeria. » Environmental Health Perspectives 124 (9): 1471-78. https://doi.org/10.1289/ehp.1510145. https://dx.doi.org/10.1289/ehp.1510145. UNEP. 2015. « Governments Agree to Set Legal Limits on Lead in Paint in Africa. ». https://www.unep.org/news-and-stories/story/governments-agree-set-legal-limits-lead-paint-africa. --. 2017. « Turning tragedies into opportunities: Overcoming Africa’s lead challenge. ». https://www.unep.org/news-and-stories/story/turning-tragedies-opportunities-overcoming-africas-lead-challenge. --. 2020. « Update on the Global Status of Legal Limits on Lead in Paint December 2020. ». https://saicmknowledge.org/. --. 2021. « Era of leaded petrol over, eliminating a major threat to human and planetary health. ». https://www.unep.org/news-and-stories/press-release/era-leaded-petrol-over-eliminating-major-threat-human-and-planetary. Vorvolakos, Th, S. Arseniou, et M. Samakouri. 2016. « There is no safe threshold for lead exposure: Α literature review. » Psychiatriki 27 (3): 204-14. https://doi.org/10.22365/jpsych.2016.273.204. https://dx.doi.org/10.22365/jpsych.2016.273.204. Wolf, J., R. B. Johnston, A. Ambelu, B. F. Arnold, R. Bain, M. Brauer, J. Brown, B. A. Caruso, T. Clasen, J. M. Colford, Jr., J. E. Mills, B. Evans, M. C. Freeman, B. Gordon, G. Kang, C. F. Lanata, K. O. Medlicott, A. Prüss-Ustün, C. Troeger, S. Boisson, et O. Cumming. 2023. « Burden of disease attributable to unsafe drinking water, sanitation, and hygiene in domestic settings: a global analysis for selected adverse health outcomes. » Lancet 401 (10393): 2060-71. https://doi.org/10.1016/s0140-6736(23)00458-0. Yu, D. 2025a. Spatial Data Analysis With R . SAGE Publications. Yu, D.L., et Y. D. Wei. 2008. « Spatial data analysis of regional development in Greater Beijing, China, in a GIS environment. » Papers in Regional Science 87 (1): 97-117. https://doi.org/10.1111/j.1435-5957.2007.00148.x. ://WOS:000254735000006 . Yu, Danlin. 2025b. « Lead exposure in the 21st century: Modeling a path from crisis to prevention. » Eco-Environment & Health : 100159. https://doi.org/https://doi.org/10.1016/j.eehl.2025.100159. https://www.sciencedirect.com/science/article/pii/S2772985025000286. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 31 Jan, 2026 Reviews received at journal 07 Nov, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviewers invited by journal 03 Nov, 2025 Editor assigned by journal 14 Aug, 2025 Submission checks completed at journal 07 Aug, 2025 First submitted to journal 19 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7161842","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":542118647,"identity":"5b07a57d-c4a7-4e6b-a52e-9e44e61ebaf5","order_by":0,"name":"Gift Fabolude","email":"","orcid":"","institution":"Montclair State University","correspondingAuthor":false,"prefix":"","firstName":"Gift","middleName":"","lastName":"Fabolude","suffix":""},{"id":542118648,"identity":"b6828112-883a-4d2c-8d49-86eebf085877","order_by":1,"name":"Charles Knoble","email":"","orcid":"","institution":"Montclair State University","correspondingAuthor":false,"prefix":"","firstName":"Charles","middleName":"","lastName":"Knoble","suffix":""},{"id":542118649,"identity":"1cc1220a-8d66-4c7d-84f9-35566531db1a","order_by":2,"name":"Anvy Vu","email":"","orcid":"","institution":"Montclair State University","correspondingAuthor":false,"prefix":"","firstName":"Anvy","middleName":"","lastName":"Vu","suffix":""},{"id":542118650,"identity":"bb282e43-dc15-4f6e-b404-59f96ae5337d","order_by":3,"name":"Isoken Aighewi","email":"","orcid":"","institution":"University of Benin","correspondingAuthor":false,"prefix":"","firstName":"Isoken","middleName":"","lastName":"Aighewi","suffix":""},{"id":542118651,"identity":"5cc7a686-f567-496d-818c-01f2f91e080a","order_by":4,"name":"Danlin Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYHACwwcMDBIyUI4FAxuI4sGvxdgAqAWi5gCDBFFazCTgakBaGAhpMbiRvK3qRo0FD4PY4WOPP9RIyPNJH2B88LYNn5a0sts5x4AOk05LNzhwTMKwjS+B2XAuXi05Zrdz2EBacswkDjZIMLbxMLBJ8xLQUpzzD6HFHqiF/TchLcy5bQgtiSBbmPFpkTzzrFg6t0+Ch006LU3izDGJ5DYexmbJOedwa+E7nrzxc863Ojl+6eRjEhU1Nrbze5gPfnhThluLwgEogw0hxtiAWz0QyOOXHgWjYBSMglEABABO4EWH7Qhr+AAAAABJRU5ErkJggg==","orcid":"","institution":"Montclair State University","correspondingAuthor":true,"prefix":"","firstName":"Danlin","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-07-19 04:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7161842/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7161842/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95845630,"identity":"5306ee7e-11e4-4d48-b966-9ca6c575454f","added_by":"auto","created_at":"2025-11-13 14:50:20","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165984,"visible":true,"origin":"","legend":"","description":"","filename":"Figures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7161842/v1/1e66285ad86865d1060a5625.docx"},{"id":95845626,"identity":"257323f6-6bca-4883-a5ec-a2c392e5cdd9","added_by":"auto","created_at":"2025-11-13 14:50:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129990,"visible":true,"origin":"","legend":"","description":"","filename":"NatureHumanitiesandSocialSciencesCommunications8525.docx","url":"https://assets-eu.researchsquare.com/files/rs-7161842/v1/b09925a89cbb657da5cbe3bb.docx"},{"id":95845625,"identity":"3dcdf2f6-58e2-4290-bf36-95435d60cde7","added_by":"auto","created_at":"2025-11-13 14:50:20","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":25947,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7161842/v1/a50a7b68973ade2a07bcbeef.docx"},{"id":95845628,"identity":"336cfd27-7cb9-4843-9dc9-70344c88e5fa","added_by":"auto","created_at":"2025-11-13 14:50:20","extension":"json","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7180,"visible":true,"origin":"","legend":"","description":"","filename":"3e61eb2954384ff0803f5621abd7509f.json","url":"https://assets-eu.researchsquare.com/files/rs-7161842/v1/a4750e2a4625e05265956525.json"},{"id":95845633,"identity":"a8ba9f9c-4409-4437-8593-c8288168bc52","added_by":"auto","created_at":"2025-11-13 14:50:20","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":174858,"visible":true,"origin":"","legend":"","description":"","filename":"3e61eb2954384ff0803f5621abd7509f1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7161842/v1/1c8869d68f26c286e0c185ab.xml"},{"id":96240382,"identity":"183a6199-b1f4-4464-81a0-ba4ca9fc8956","added_by":"auto","created_at":"2025-11-19 07:08:52","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":228490,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7161842/v1/586205a3e3b92e0934d25fa8.jpeg"},{"id":95845627,"identity":"99946b5c-d0ae-4140-ba7f-66c0a994a326","added_by":"auto","created_at":"2025-11-13 14:50:20","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112253,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7161842/v1/35b640b2aa73e7dff02f13a7.png"},{"id":95845629,"identity":"3836af4a-a3d8-48d9-83d9-f5fbd335f07e","added_by":"auto","created_at":"2025-11-13 14:50:20","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":170061,"visible":true,"origin":"","legend":"","description":"","filename":"3e61eb2954384ff0803f5621abd7509f1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7161842/v1/b7405d24ff3cac9007ee0e46.xml"},{"id":96240894,"identity":"ff73c8fa-89ca-455f-80a7-fd7f7e0dc61b","added_by":"auto","created_at":"2025-11-19 07:09:39","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":181348,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7161842/v1/2981c2da29d299685af2fe8e.html"},{"id":95845623,"identity":"7feb3ef9-6844-44e5-9077-1f45d9d8e538","added_by":"auto","created_at":"2025-11-13 14:50:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79629,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of premature deaths caused by lead exposure in Africa.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7161842/v1/554d4a963619c5b591a3ca04.png"},{"id":95845624,"identity":"0b7ccb1c-33e5-4eca-a06d-d2a0303a5c20","added_by":"auto","created_at":"2025-11-13 14:50:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34693,"visible":true,"origin":"","legend":"\u003cp\u003eTop 20 countries in Africa with the highest premature deaths caused by lead exposure.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7161842/v1/9d82bdbe5160d1ad803f0c6c.png"},{"id":96255034,"identity":"14a5ef58-545b-4b72-b9d0-a277cf7ae255","added_by":"auto","created_at":"2025-11-19 07:47:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1203265,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7161842/v1/2bfdea41-8b35-44b9-84c3-fba2d1d11ca5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development, Trade and Environmental Justice: Decoupling Economic Growth from Lead Mortality in Africa","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLead, a toxic heavy metal, remains a serious environmental health threat in many parts of Africa. Unlike in high-income countries where aggressive regulations have dramatically reduced lead exposure, African populations continue to experience significant lead burdens​ (Oulhote \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bede-Ojimadu, Amadi, et Orisakwe 2018). Currently, no level of lead exposure is considered safe, and even low levels can harm health, particularly impacting neurodevelopment ​(Bede-Ojimadu, Amadi, et Orisakwe 2018; Vorvolakos, Arseniou, et Samakouri 2016; Danlin Yu \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). From a social‑science standpoint, Africa exemplifies a \u0026ldquo;pollution haven\u0026rdquo; dynamic wherein regulatory gaps externalize health costs (Copeland \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). At the same time, Environmental Kuznets Curve (EKC) theory predicts a turning‑point once institutions strengthen (Kordas et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This study positions lead mortality at that intersection, posing two questions: (1) How do economic and trade indicators jointly predict mortality? (2) Which infrastructural or regulatory levers disrupt that coupling?\u003c/p\u003e\u003cp\u003eLead toxicity poses a particularly severe threat to vulnerable populations, especially children, whose developing nervous systems are highly susceptible to permanent damage. Exposure during critical developmental windows can result in irreversible neurocognitive deficits, reduced IQ, behavioral problems (such as decreased attention span and increased frustration), and diminished lifetime earning potential (Lanphear et al. 2005).\u003c/p\u003e\u003cp\u003eThe sources of lead exposure across Africa are diverse and widespread. They include deteriorating lead-based paints, contaminated soils, informal used lead-acid battery (ULAB) recycling, informal electronic waste (e-waste) processing, lead-glazed ceramics and cookware, lead-soldered food cans, contaminated spices (especially turmeric adulterated with lead chromate), and traditional medicines or cosmetics containing lead (A. Mathee \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In many regions, rapid urbanization and industrialization have occurred without adequate environmental safeguards or enforcement capacity, potentially increasing population exposure to this neurotoxicant. The pace of urbanization across many African nations has often outpaced regulatory frameworks, resulting in land use patterns that increase population exposure to lead (Suk et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; D\u0026oacute;rea \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For instance, the common practice of situating repair shops, small-scale manufacturing, and informal recycling operations within densely populated residential areas creates direct pathways for lead exposure (Poudel et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the relationship between urbanization and lead exposure can be complex, with some studies reporting higher blood lead levels (BLLs) in rural settings potentially linked to factors like specific agricultural practices, geophagy, or nutritional deficiencies enhancing lead absorption (A. Mathee et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Forsyth et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn countries like Nigeria and Ghana, economic necessity drives informal recycling sectors (ULAB and e-waste) that expose vulnerable populations, particularly children, to dangerous levels of lead. Children from impoverished households often scavenge damaged electronic goods from dumpsites receiving e-waste, sometimes imported from developed countries (Abogunrin-Olafisoye et Adeyi 2025; P\u0026uuml;schel et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These young scavengers dismantle items like computers, televisions, mobile phones, and batteries, often using rudimentary tools or bare hands without protective equipment, leading to direct exposure to lead dust and fumes from burning components. Informal recycling frequently occurs near homes, creating continuous exposure pathways (Lebbie et al. 2021). BLLs in children from these communities often significantly exceed international safety thresholds (A. Mathee \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Danlin Yu \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). This exposure contributes to developmental delays, cognitive impairments, and behavioral problems, potentially reinforcing cycles of poverty (Desye et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pascale et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; P\u0026uuml;schel et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe mining industry, particularly artisanal and small-scale mining (ASM), also represents a significant source of lead exposure for workers and communities in countries like Nigeria, Zambia, South Africa, and Tanzania (Brunnschweiler, Karapetyan, et Lujala 2024; Ondayo et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Ore processing often occurs with minimal safety precautions, exposing miners (including children in some ASM operations) to lead-laden dust, which can be carried home. The 2010 lead poisoning disaster in Zamfara, Nigeria, linked to informal gold mining releasing associated lead minerals, resulted in hundreds of child deaths and widespread poisoning (Lo et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Even in more formalized operations, inadequate enforcement can lead to contamination of air, soil, and water from mining tailings and waste, creating long-term exposure risks for nearby communities (Lo et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tirima et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite growing recognition of lead\u0026rsquo;s public health significance, understanding the interplay of factors influencing lead exposure and mortality in Africa remains limited. This study aims to provide insights through a commentary review and a cross-country analysis examining how economic development, regulatory policies, environmental conditions, public awareness, and infrastructure quality collectively shape lead exposure mortality patterns. We specifically investigate the relationship between premature deaths attributed to lead exposure (using GBD estimates) and potential determinants: GDP, lead paint ban status, public awareness (proxied by Google search trends), vegetation coverage (NDVI), imports, and sanitation infrastructure.\u003c/p\u003e\u003cp\u003eThis study aims to contribute to environmental health policy by identifying potential leverage points for intervention and highlighting the \u0026ldquo;development paradox\u0026rdquo; where economic progress without adequate environmental health safeguards may increase the lead burden. By examining this complex issue, we hope to inform more effective, context-specific strategies to reduce lead exposure in the Africa continent.\u003c/p\u003e\u003cp\u003eThe manuscript is organized as follows. After this introduction section, we will briefly review lead exposure in African countries, existing governmental regulation and intervention, and its economic and financial implications. Data and methods are introduced next. Results from the analysis are presented in the fourth section, which is followed by a detailed discussion. We conclude our study with a summary and reflection of the limitations presented in the current study, aiming for better exploration in the future.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Extent of Lead Exposure in African Countries\u003c/h2\u003e\u003cp\u003eBLLs in African populations, especially children, are alarmingly high in many settings (Moya-Alvarez et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Studies consistently show a large proportion of children with BLLs above the widely recognized reference level of 5 \u0026micro;g/dL. For example, surveys in the 1990s found over 90% of children in Cape Province, South Africa had BLLs\u0026thinsp;\u0026ge;\u0026thinsp;10 \u0026micro;g/dL (Nriagu, Blankson, et Ocran 1996).\u003c/p\u003e\u003cp\u003eBeyond blood measurements, the burden of disease attributable to lead is significant. In 2017, lead exposure was estimated to account for over 54% of developmental intellectual disability burden in children under 5 in Sub-Sahara Africa (SSA) (Bede-Ojimadu, Amadi, et Orisakwe 2018). Lead also contributes substantially to premature deaths via cardiovascular and other diseases. However, surveillance is limited. Except perhaps for South Africa, most African nations lack systematic data on population lead levels (Oulhote \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This data scarcity likely means the true extent of lead poisoning is under-recognized.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Government Regulations and Interventions\u003c/h2\u003e\u003cp\u003eAfrican governments, often with international partners, have undertaken interventions against lead exposure. The most successful has been the phase-out of leaded gasoline. Spurred by global initiatives led by UNEP\u0026rsquo;s Partnership for Clean Fuels and Vehicles, all African countries eliminated lead additives in petrol between the early 2000s and 2021 (UNEP \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, air lead levels dropped, and BLLs in recent generations are lower than in the 1980s\u0026ndash;90s (Angela Mathee et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLead paint regulation is another critical area. Lead paint remains widely available, but progress is being made. South Africa pioneered regulation in 2009 (SAMRC \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).​ By 2015, officials from 15 African countries aimed for a 90 ppm lead limit by 2020, the level recommended by the WHO/UNEP Global Alliance to Eliminate Lead Paint​ (UNEP \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Regional bodies like the East African Community and Economic Community of West African States (ECOWAS) moved towards harmonized standards. However, as of late 2021, only about 7 African countries (13%) had legally binding controls, often with varying standards. Countries like Kenya, Tanzania, Ethiopia, Cameroon, C\u0026ocirc;te d\u0026rsquo;Ivoire, Ghana, and Morocco have made recent progress. Enforcement remains a challenge even where laws exist, requiring capacity to monitor compliance for both domestic and imported paints (UNEP \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; SAMRC \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAddressing lead in consumer products (toys, ceramics, cosmetics) and contaminated sites involves mixed interventions. Some governments have import controls or standards (e.g., Tunisia, Morocco on kohl; Nigeria on pottery glazes), but enforcement varies. International agencies (WHO, UNICEF) and NGOs have run awareness programs and product testing campaigns (e.g. checking lead content in paints and toys in markets, then publicizing the results to spur government action) with mixed success (UNEP \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEmergency clean-ups followed acute poisoning events, like in Zamfara (Nigeria) in 2010, involving government and international partners (WHO, MSF) for soil remediation and chelation treatment 26 (UNEP, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Similarly, Senegal removed contaminated soil and relocated informal battery recycling after a 2008 tragedy (UNEP \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Financial and Economic Implications\u003c/h2\u003e\u003cp\u003eLead poisoning presents significant financial and economic challenges in Africa, exacerbated by policy weaknesses, the large informal sector, and rapid urbanization. Informal activities like ULAB and e-waste recycling contribute substantially to exposure due to inadequate regulation and enforcement (Oulhote \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Economic development, urbanization and industrialization, if pursued without adequate environmental protection, can increase lead exposure risks (Kordas et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pennington et al. 2024). The economic burden is likely substantial but understudied in Africa. In the US, childhood lead poisoning incurs estimated annual costs of \u003cspan\u003e$\u003c/span\u003e76.6\u0026nbsp;billion in healthcare and productivity losses (Attina et Trasande 2013), with adult occupational exposure adding further costs (Levin \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Given likely much higher exposure levels in many African settings, the economic toll is potentially far greater.\u003c/p\u003e\u003cp\u003eInvesting in lead exposure prevention is an economic imperative, not just a public health necessity. Cost-benefit analyses, primarily from high-income settings but conceptually relevant globally, demonstrate substantial returns. Studies estimate that every \u003cspan\u003e$\u003c/span\u003e1 invested in lead hazard control yields returns ranging from \u003cspan\u003e$\u003c/span\u003e17 to \u003cspan\u003e$\u003c/span\u003e221 (Gould \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). These gains arise from avoided healthcare costs, reduced special education needs, increased productivity and lifetime earnings, higher tax revenues, and lower crime rates (Gould \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Despite these potential returns, many African governments face financial constraints and competing development priorities, hindering implementation and enforcement of lead regulations. A critical issue is addressing the informal sector involved in activities like ASM or ULAB/e-waste recycling, which are often driven by poverty (Desye et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Outright bans risk worsening economic hardship. A preferable approach involves providing financial and technical incentives for transitions to safer practices, such as microfinance, support for safer recycling economies, and employment in remediation, ensuring health protection aligns with livelihood needs. Overcoming these challenges requires strategic integration of regulation, investment, and economic incentives to make lead mitigation economically viable and sustainable (Danlin Yu \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 DevelopmentEnvironment Theories\u003c/h2\u003e\u003cp\u003eBuilding on the preceding review of Africa\u0026rsquo;s lead-exposure burden and regulatory gaps, we now synthesize the three complementary lenses, Pollution Haven, Environmental Kuznets Curve (EKC) and Environmental Justice (EJ), to frame the study\u0026rsquo;s hypotheses.\u003c/p\u003e\u003cp\u003ePollution-Haven logic posits that multinational firms redirect hazardous production to jurisdictions where environmental standards and enforcement costs are lowest. In the African context, expanding imports of used batteries, ewaste and lowgrade pigments effectively externalize toxic risks from the global North to the global South. As a result, GDP growth that is heavily trade‐mediated can raise lead‐related mortality even when domestic industrial output remains modest.\u003c/p\u003e\u003cp\u003eThe EKC hypothesis refines this view by introducing an institutional turning point: early-stage growth often intensifies pollution, but beyond a threshold of income, governance capacity and citizen demand for clean environments strengthen, bending the curve downward. Whether African economies have reached or can accelerate toward this governance inflection is a central empirical question.\u003c/p\u003e\u003cp\u003eFinally, Environmental-Justice theory emphasizes spatial and social inequities in the distribution of environmental harms. Colonial trade corridors, urban-rural divides and informalsector recycling concentrate lead exposure among children and lowincome populations, turning aggregate growth into a regressive health burden.\u003c/p\u003e\u003cp\u003eSynthesizing these perspectives, we advance a trademediated toxicity pathway: rising GDP and import volumes elevate lead risk until governance, infrastructure (e.g., sanitation) and public awareness (proxied by Google-Trends searches) catch up. This integrative lens motivates our three propositions, namely, GDP\u0026ndash;mortality coupling, import‐driven toxicity transfer and infrastructurebased mitigation, and shapes the spatial\u0026ndash;econometric strategy we will detail next.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Data and methods","content":"\u003cp\u003eThe extent of lead exposure in Africa necessities the analysis of its determinants for better lead prevention policies. This study explores the relationships between lead exposure mortality and multiple socioeconomic and environmental factors across African nations. By examining GDP, lead paint regulations, public awareness metrics, imports, greenery, and sanitation infrastructure, we seek to identify factors associated with lead exposure deaths. This multifactorial approach aims to move beyond simplistic narratives, understanding how development pathways, environmental conditions, and policies collectively shape lead exposure risk. Our findings intend to provide evidence-based guidance for interventions, recognizing diverse contexts and potential unintended consequences of economic development lacking adequate environmental health safeguards.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data sources\u003c/h2\u003e\u003cp\u003eData used in this analysis is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Each of these variables captures a distinct dimension, ranging from health burden, economic drivers, environmental mediators, infrastructure capacity, policy interventions, societal awareness, and trade pathways, that together form a comprehensive model for explaining cross-country variation in lead exposure mortality in Africa. While it is our intention to include the most recent data available, we have to adjust for the different years that there are sufficient data. Detailed descriptions of the data source follow.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePredictor Variable Summary\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDefinition/Operationalization\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eData Source\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYear(s) Used\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHypothesized Relationship with Lead Mortality\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependent Variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of premature deaths attributed to lead exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIHME Global Burden of Disease (GBD) Study (via Our World in Data)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIndependent Variables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGross Domestic Product estimates\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNatural Earth / World Bank (Authors to specify exact source \u0026amp; metric, e.g., GDP per capita PPP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMillions USD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+/- (Ambiguous: EKC suggests initial +, then -; Development paradox suggests +)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLead Paint Ban Status (Lead paint)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBinary: 1\u0026thinsp;=\u0026thinsp;Legally-binding controls exist; 0\u0026thinsp;=\u0026thinsp;No such controls exist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWHO Global Health Observatory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0/1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e- (Effective regulation should reduce mortality)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic Awareness (Google index)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage relative search volume for \"lead exposure\" + \"lead poisoning\" (weighted for internet penetration)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGoogle Trends\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndex (0-100, relative)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2004\u0026ndash;2021 (average)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e- (Higher awareness might lead to protective behavior/policy demand) \u003cem\u003eNote: Limitations apply to this proxy.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormalized Difference Vegetation Index (proxy for vegetation density/health)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNASA MODIS (MOD13Q1 V6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndex (-1 to 1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e- (Vegetation might reduce dust/erosion)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImports\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal import value (proxy for trade engagement/potential import of contaminated goods)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInternational Trade Centre (ITC) Trade Map\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+ (Imports, esp. e-waste, can be exposure source)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSanitation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShare of population using at least basic sanitation services (proxy for infrastructure/public health capacity)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWHO/UNICEF JMP (via Our World in Data)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e- (Better infrastructure might correlate with lower exposure/better health systems)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is about here]\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1 Number of premature deaths from lead exposure\u003c/h2\u003e\u003cp\u003eThe primary outcome variable, the number of premature deaths attributed to lead exposure (LED), represents the estimated number of deaths where lead exposure is a contributing risk factor, accounting for associated outcomes like cardiovascular and kidney diseases. This variable directly measures the health burden of lead exposure at a national level. The Institute for Health Metrics and Evaluation (IHME) estimates are widely used in global health research to quantify disease burden and attributable mortality, providing a standardized metric essential for cross-country comparisons of lead\u0026rsquo;s impact. Data was obtained from the IHME Global Burden of Disease (GBD) study via Our World in Data (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results/\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). IHME uses complex modeling incorporating exposure data, relative risks, and mortality rates, adjusting for age structures to yield a comparable, cross-national metric of mortality attributable to lead, essential for assessing the health impact of environmental exposures in low‐ and middle‐income settings. The primary limitations of this data are that GBD estimates for regions with sparse primary data, like parts of Africa, rely heavily on modeling and imputation, introducing uncertainty.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2 Economic indicators (national GDP)\u003c/h2\u003e\u003cp\u003eGross Domestic Product (GDP) data was acquired from Natural Earth (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.naturalearthdata.com/\u003c/span\u003e\u003cspan address=\"https://www.naturalearthdata.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). GDP (estimates in millions USD) serves as a fundamental indicator of a nation\u0026rsquo;s level of economic development. While popular, using Aggregate GDP is admittedly a crude measure for national development, not capturing income distribution, economic structure, or governance quality, which might independently affect environmental health. Still, at the current research design, this crude measure suffices our initial understanding between national development and lead exposure caused premature death.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3 Normalized Difference Vegetation Index (NDVI)\u003c/h2\u003e\u003cp\u003eNDVI provides a standardized, remotely sensed measure of vegetation density and health. Dense vegetation can reduce airborne dust resuspension and act as a barrier to lead‑contaminated soil particulates, thereby mitigating exposure pathways, particularly in rapidly urbanizing areas where vegetation cover influences ambient lead dust levels (Frndak et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). NDVI data from NASA\u0026rsquo;s MODIS sensor (MOD13Q1 V6, 250m resolution, 16-day composites) was used as a proxy for national environmental conditions in this study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.1.4 Sanitation infrastructure\u003c/h2\u003e\u003cp\u003eAdequate sanitation systems not only reduce fecal-oral disease transmission but also serve as proxies for broader investment in water, waste, and environmental management, factors linked to lower exposure to soil and water pollutants, including lead (Selendy \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Data on the \u0026ldquo;Share of the population using at least basic sanitation services\u0026rdquo; from the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) via Our World in Data served as a proxy for infrastructure development and public health capacity (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ourworldindata.org/grapher/share-of-population-with-improved-sanitation-faciltities\u003c/span\u003e\u003cspan address=\"https://ourworldindata.org/grapher/share-of-population-with-improved-sanitation-faciltities\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The JMP estimates are based on a combination of household surveys, censuses, and administrative data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.1.5 Lead paint regulation\u003c/h2\u003e\u003cp\u003eLegally binding controls on lead in paint directly target one of the most persistent and widespread sources of childhood and adult lead exposure (Dignam et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Whether a country has adopted and enforces a lead-paint limit is a critical policy lever; evaluating its presence (binary) allows assessment of lead-related regulation\u0026rsquo;s role in reducing longterm exposure burdens. Data on legally-binding controls on lead paint for 2023 was sourced from the WHO Global Health Observatory (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/data/gho/data/themes/topics/indicator-groups/legally-binding-controls-on-lead-paint\u003c/span\u003e\u003cspan address=\"https://www.who.int/data/gho/data/themes/topics/indicator-groups/legally-binding-controls-on-lead-paint\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The regulatory status information was transformed into a binary variable (Leadpaint) indicating lead paint ban as 1 and use of lead paint as 0.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.1.6 Google Index for Lead Awareness\u003c/h2\u003e\u003cp\u003eIn contexts where systematic survey data on awareness are lacking, Google Trends has been shown to capture population-level interest shifts, providing insight into when and where lead becomes a public concern (Fu et Miller 2022; Ginsberg et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Based on a previous study in Nigeria (Fabolude et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), Google Trends relative search volume (RSV) for \u0026ldquo;lead exposure\u0026rdquo; and \u0026ldquo;lead poisoning\u0026rdquo; (averaged 2004\u0026ndash;2021, weighted for internet penetration) are used in this study to proxy public awareness. In this study, we do realize some potential limitations of utilizing this proxy for lead public awareness, such as its only reflecting Internet active population\u0026rsquo;s awareness, possible language/categorization issues, and potential noises using standard normalization approach (Knoble et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Knipe et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We still employ this proxy because of the lacking of other systematically collected data. We hope this \u0026ldquo;big data\u0026rdquo; approach could provide some insight into the relative level of public information-seeking behavior related to lead hazards across African nations, as often employed when data is scarce (Thompson et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.1.7 Imports data\u003c/h2\u003e\u003cp\u003eTotal import values serve as an indicator of global trade engagement and potential inflow of lead‑containing products (e.g., e‑waste, leaded ceramics) (Chen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). High import volumes may reflect pathways through which hazardous materials enter domestic markets, particularly where regulatory oversight is weak (Baggs \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), giving rise to informal recycling activities and increased exposure. Total import values (USD) from the International Trade Centre (ITC) Trade Map (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.trademap.org/Index.aspx\u003c/span\u003e\u003cspan address=\"https://www.trademap.org/Index.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used as an indicator of trade engagement and potential pathways for lead-containing products (e.g., e-waste). We extracted total import values in dollars for each African country, representing the monetary value of all goods and materials imported annually.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Methodology\u003c/h2\u003e\u003cp\u003eMultivariate linear regression analysis was employed using R (version 4.5.0) to examine the relationship between premature deaths from lead exposure (LED) and the independent variables: Leadpaint, GDP, Googleindex, NDVI, Imports, and Sanitation. Prior to the multivariate regression, preliminary exploratory data analysis was conducted. The dependent variable, LED was found to be highly skewed to the left (large numbers of small values), a natural logarithmic transformation makes the dependent variable approximately normal. Scatterplots between the log-transformed LED and all independent variables also suggest that log-transformation of GDP and Imports show reasonably well linear relationships with log-transformed LED. For the other variables, the linear relationships are sufficient to build the model. Based on this preliminary exploration, a log-log specification of the model is built for final evaluation. Specifically, the model takes the form:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:log\\left(LED\\right)={\\beta\\:}_{0}+{\\beta\\:}_{1}\\text{log}\\left(GDP\\right)+{\\beta\\:}_{2}Leadpaint+{\\beta\\:}_{3}Googleindex+{\\beta\\:}_{4}NDVI+{\\beta\\:}_{5}Imports+{\\beta\\:}_{6}sanitation+\\epsilon\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn addition to the multivariate regression model, we also examine potential interaction effects between the lead paint regulation status (the binary variable) and the GDP, Imports and awareness level (proxied by Google Trends). These interaction terms were included to explore whether the impact of economic development (GDP), import volumes, and public awareness (Google Index) on lead mortality differ systematically between countries that have a legally binding lead paint ban and those that do not.\u003c/p\u003e\u003cp\u003eFurthermore, since we are using geographical information for regression analysis, it is customary to test the regression residuals for potential spatial autocorrelation (D.L. Yu et Wei 2008). Because our study is on the national level, and there is an island nation (Madagascar), to avoid overly imbalanced weight matrix structure or island effect, we designed a sphere of influence approach that is based on the Delaunay Triangulation to construct the spatial weights matrix, addressing potential issues with island/non-contiguous territories (D. Yu \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). The residuals\u0026rsquo; spatial autocorrelation is tested through Moran\u0026rsquo;s Index using the spdep package in R. If spatial autocorrelation is detected, the Lagrange Multiplier test is conducted to determine which specification (spatial lag or spatial error) would be more appropriate. Technical details are abundant and will not be repeated here. Interested scholars are encouraged to consult D. Yu (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e) for more details.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Spatial Distribution of LED\u003c/h2\u003e\u003cp\u003eThe spatial distribution of LED in 2021 showed substantial geographic variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). North and Northeast Africa exhibited the highest burden, with Egypt reporting the highest estimated mortality (38,000 deaths). West Africa generally showed moderate to low rates, except for Nigeria (second highest, \u0026gt;\u0026thinsp;12,000 deaths). Morocco (10,500), Ethiopia (10,000), and Algeria (9,800) followed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This distribution highlights lead exposure as a significant public health issue across diverse African regions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e[Figures \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e are about here]\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Regression analysis and diagnostics\u003c/h2\u003e\u003cp\u003eThe multivariate linear regression model using the log-log specification has produced rather interesting results, which is reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Multicollinearity is checked with variance of inflation (VIF) and also reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The three interaction terms (Leadpaint with GDP, Imports, and Googleindex) are included one at a time and all at once (models 2\u0026ndash;5). We expect that legally-binding lead paint bans will strengthen the influence of economic development, import volumes, and public awareness on reducing premature lead exposure mortality. However, results suggest that except for the interaction term of Leadpaint with Googleindex, the inclusion of other two interaction terms causes severe multicollinearity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which substantially inflates the standard errors of the associated coefficients, rendering their individual estimates unstable and making it difficult to reliably interpret their distinct impacts on lead mortality. The inclusion of the interaction term between Leadpaint with Googleindex, on the other hand, did not improve the model sufficiently to warrant the specification based on the Akaike Information Criterion (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate Regression Model results, VIF, and Spatial Diagnostics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1) Base OLS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBase OLS VIF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2) GDP \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e LP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(3) Imports \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e LP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(4) Google \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e LP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(5) All 3 Interactions\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.341\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.878)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(1.018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.856)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.953)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elog(GDP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.560***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.470**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.553***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.516***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.303*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.134)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.135)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.134)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.133)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.139)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elog(Imports)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.379*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.340*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.323+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.449**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.528**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.153)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.148)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.163)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.154)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.157)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoogle Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.016+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.014+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.015+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.008)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.274*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.695)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.668)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.698)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.681)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.625)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSanitation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.023***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.024***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.023***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.026***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.029***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeadpaint (LP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.353*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-3.882**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.271)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(1.539)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.387)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.360)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(1.430)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e LP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.305*\u003c/p\u003e\u003cp\u003e(0.140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.762*\u003c/p\u003e\u003cp\u003e(0.283)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImports \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e LP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003cp\u003e(0.156)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.443\u003c/p\u003e\u003cp\u003e(0.313)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoogle Index \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e LP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.036+\u003c/p\u003e\u003cp\u003e(0.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.044*\u003c/p\u003e\u003cp\u003e(0.019)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNum. Obs.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVIF: LP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e42.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVIF: GDP \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e LP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e218.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVIF: Imports \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e LP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e176.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVIF: Google Index \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e LP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e129.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e126.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e130.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e128.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e119.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eMoran\u0026rsquo;s I (Residuals of Base OLS): 0.259, p-value\u0026thinsp;=\u0026thinsp;0.002\u003c/p\u003e\u003cp\u003eLM Error (Rao Score): 6.841 (df\u0026thinsp;=\u0026thinsp;1), p-value\u0026thinsp;=\u0026thinsp;0.009\u003c/p\u003e\u003cp\u003eLM Lag (Rao Score): 6.515 (df\u0026thinsp;=\u0026thinsp;1), p-value\u0026thinsp;=\u0026thinsp;0.011\u003c/p\u003e\u003cp\u003eRobust LM Error (Rao Score): 1.758 (df\u0026thinsp;=\u0026thinsp;1), p-value\u0026thinsp;=\u0026thinsp;0.185\u003c/p\u003e\u003cp\u003eRobust LM Lag (Rao Score): 1.432 (df\u0026thinsp;=\u0026thinsp;1), p-value\u0026thinsp;=\u0026thinsp;0.231\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eSignificance symbol: + p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn addition, the Moran\u0026rsquo;s Index for the model\u0026rsquo;s residual show significant spatial autocorrelation (Moran\u0026rsquo;s I\u0026thinsp;=\u0026thinsp;0.259, p-value\u0026thinsp;=\u0026thinsp;0.002). The Lagrange Multiplier test is then conducted, and the Rao\u0026rsquo;s Scores for both spatial error and spatial lag models are also reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The test suggests tha a spatial error model (SEM) provides a more appropriate alternative, suggesting that there are missing variables that are significantly spatially autocorrelated. The results of the SEM are reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of Spatial Error autoregressive model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePr(\u0026gt;|z|)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.597\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elog(GDP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.557***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.783\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elog(Imports)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.342**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLead paint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.932\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoogle Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.014+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.630\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSanitation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.019***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eLambda (Spatial Error Parameter)\u003c/p\u003e\u003cp\u003eEstimate: 0.50322***\u003c/p\u003e\u003cp\u003eAsymptotic standard error: 0.136\u003c/p\u003e\u003cp\u003ez-value: 3.712, p-value: 0.0002\u003c/p\u003e\u003cp\u003eLR test (vs OLS): 7.7534, p-value: 0.0053612\u003c/p\u003e\u003cp\u003eWald statistic: 13.7805, p-value: 0.0002055\u003c/p\u003e\u003cp\u003eLog likelihood: -53.05513 for error model\u003c/p\u003e\u003cp\u003eNumber of observations: 52\u003c/p\u003e\u003cp\u003eAIC: 124.11, (AIC for lm: 129.86)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eSignificance symbol: + p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e[Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e are about here]\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe results presented Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e paint an interesting and farily revealing story about economic development, international trade, public infrastructure, and awareness-related factors in shaping lead exposure caused mortality. In the following subsections, we interpret these results in detail and discuss relevant policy implications.\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e5.1. Advancing the Analysis: The Imperative of a Spatial Econometric Approach\u003c/h2\u003e\u003cp\u003eOur analysis began with an Ordinary Least Squares (OLS) regression as a baseline (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Model 1). After examining the interaction terms and deciding that the inclusion is not well justified (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, models 2\u0026ndash;5), we conducted diagnostic tests on the OLS residuals. The test revealed a Moran\u0026rsquo;s \u003cem\u003eI\u003c/em\u003e of 0.259 (p\u0026thinsp;=\u0026thinsp;0.002), indicating significant positive spatial autocorrelation in the residuals, which is commonly the case when geographically referenced data is used for analysis. Lagrange Multiplier (LM) tests reinforced the need for a spatial error model (Rao\u0026rsquo;s score for error model\u0026thinsp;=\u0026thinsp;6.84, p\u0026thinsp;=\u0026thinsp;0.009). Although robust LM tests were less conclusive (p\u0026thinsp;\u0026gt;\u0026thinsp;0.1), the strong Moran\u0026rsquo;s \u003cem\u003eI\u003c/em\u003e and standard LM results motivated the use of an SEM (D. Yu \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). This suggests that there are other spatially autocorrelated but unobserved factors contributing to the variance of premature mortality attributable to lead exposure.\u003c/p\u003e\u003cp\u003eNot surprisingly, the spatial error coefficient (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:\\)\u003c/span\u003e\u003c/span\u003e) is ~\u0026thinsp;0.503 and significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming that a portion of variance in national lead mortality is explained by unobserved factors that are spatially correlated. The baseline model did not include those pieces of information, either due to lack of data or lack of systematic monitoring. The SEM provides a better fit (AIC\u0026thinsp;=\u0026thinsp;124.11) than the non-spatial OLS model (AIC\u0026thinsp;=\u0026thinsp;129.86), suggesting that accounting for spatial dependency improves explanatory power.\u003c/p\u003e\u003cp\u003eThis result suggests that, in essence, lead poisoning risks are not independent country by country; there are region-specific influences that affect mortality. These could include regional industry patterns (for example, clusters of mining activity in West Africa or Southern Africa), cross-border movement of leaded products (such as regional trade in batteries or spices), or shared historical legacies (e.g., neighboring countries having similar regulations or fuel use histories). The presence of spatially correlated errors means that purely national-level analyses could misattribute some effects. Some countries\u0026rsquo; high lead burdens may be driven by very specific sources \u0026ndash; for instance, Zambia\u0026rsquo;s Copperbelt (Kabwe) with extensive lead mine tailings, or the infamous lead poisoning outbreak in Zamfara, Nigeria from artisanal gold mining (Fabolude et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Such localized crises would not be fully captured by national averages. Other potential factors include the scale of the informal economy (e.g. how prevalent backyard battery recycling or cottage industries are), urban population distribution (intensity of slum dwelling), general environmental governance and corruption levels, nutritional factors (populations with calcium/iron deficiency absorb more lead), and so on. Data on many of these aspects are scarce or difficult to quantify across all countries. Indeed, a major limitation in studying lead in Africa is the paucity of standardized data on environmental lead concentrations and BLLs. Few African nations conduct regular biomonitoring for lead; most available data come from isolated research studies or global modeled estimates (Fabolude et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lo et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This lack of direct data likely contributes to uncertainty and may cause some relationships to go undetected. Our use of proxies (like Google searches or NDVI) was in part to innovatively compensate for data gaps. Despite the lack of data, our spatial regression approach is able to provide a useful macro-level picture: it highlights broad patterns and flags where policy attention is needed, even as more granular research continues.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e5.2. The Double-Edged Sword: Economic Development, Trade, and Lead Mortality\u003c/h2\u003e\u003cp\u003eA striking finding is the positive and significant association between economic growth and lead exposure mortality. The coefficient for log(GDP) in the SEM is 0.557 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that, on average, higher national income is associated with higher lead-attributable death rates (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This counterintuitive result presents a \u0026ldquo;development paradox,\u0026rdquo; whereby economic advancement correlates with worse environmental health outcomes in the context of lead (Adu Sarfo et Tweneboah 2024).\u003c/p\u003e\u003cp\u003eSuch paradox agrees with the Environmental Kuznets Curve (EKC) theory that many African countries are experiencing the initial stage of industrialization/urbanization where pollution intensifies during early industrialization and growth before improving at later stages of development (Kupzig, Kupzig, et Meier 2024). In these early phases, rapid GDP gains often stem from expansion in manufacturing, construction, mining, and other industrial activities that, without stringent safeguards, increase environmental lead contamination. For example, resource extraction and processing (such as lead mining or artisanal gold mining) can release lead into the environment, as seen in Nigeria\u0026rsquo;s mining regions (Fabolude et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Likewise, urbanization accompanying GDP growth may involve unregulated industrial emissions and proliferation of leaded consumer products, contributing to higher population exposure. This alignment of our findings with the EKC theory has been observed in other contexts: rising incomes in low-to-middle income settings can correlate with increased pollution until regulatory and technological investments eventually catch up (Kordas et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Our results suggest that in Africa, economic growth to date has been predominantly pollution-intensive \u0026ndash; outpacing the implementation of environmental health protections. The implication is that simply growing the economy, without deliberate public health safeguards, may exacerbate lead exposure risks rather than alleviate them. This underscores the need to integrate environmental health measures early in the development process, rather than assuming improvements will automatically materialize at higher income levels.\u003c/p\u003e\u003cp\u003eInternational trade emerges as another double-edged sword. log(Imports) is also positively associated with lead mortality (coefficient 0.342, p\u0026thinsp;=\u0026thinsp;0.008 in the SEM), implying that countries with greater import volumes tend to suffer higher lead-related premature deaths. This finding supports the notion that globalization can facilitate toxic exposure pathways (Lebbie et al. 2021; Chen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). One mechanism is through the import of lead-containing products and wastes. African countries often receive used or low-cost products that contain lead \u0026ndash; for instance, electronics, batteries, cosmetics, paints, toys, and ceramics with lead components (Chen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A considerable portion of the world\u0026rsquo;s electronic waste (e-waste) is shipped to parts of Africa under the guise of second-hand goods or recycling, but in reality, much of it is unsafely processed in informal sectors. This informal e-waste recycling \u0026ndash; exemplified by sites like Agbogbloshie in Accra, Ghana \u0026ndash; releases lead and other toxic metals into surrounding communities (Lebbie et al. 2021; P\u0026uuml;schel et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Mismanaged e-waste and used lead-acid batteries are identified as \u003cem\u003e\u0026ldquo;one of the most alarming sources of lead poisoning\u0026rdquo;\u003c/em\u003e in Africa, as informal recycling releases lead particles into soil and air (Muhavani et Mangwiro 2025). The positive import effect in our model resonates with the Pollution Haven Hypothesis (Copeland \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), which suggests that hazardous materials and polluting industries may flow toward regions with less stringent regulations. African nations with weaker enforcement can become dumping grounds for lead-laden waste and products, inadvertently importing environmental health risks along with goods. Notably, both GDP (a proxy for domestic economic activity) and Imports (a proxy for trade exposure) remain significant in the spatial model, indicating that multiple economic avenues \u0026ndash; internal growth and global trade \u0026ndash; are contributing to the continent\u0026rsquo;s lead burden. Economic integration into global markets clearly brings benefits, but without adequate oversight on environmental regulations, it also amplifies the influx of lead hazards, from old car batteries to contaminated consumer goods. This suggests that policies must address not only domestic industrial pollution but also the transboundary movement of toxic materials.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e5.3. Public Infrastructure and Awareness: Protective Factors and Data Limitations\u003c/h2\u003e\u003cp\u003eOur model suggests that enhanced sanitation infrastructure (sanitation = \u0026minus;\u0026thinsp;0.019, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) is an important factor for reducing lead exposure mortality. Countries with better public sanitation (a higher share of the population with basic sanitation services) experience fewer deaths from lead exposure, all else being equal. Improved sanitation can be viewed as a proxy for broader investments in public health infrastructure and environmental management. Nations that are able to provide clean water, waste disposal, and hygienic living conditions likely also have better systems for managing environmental pollutants, including hazardous waste and contaminated sites. For example, proper sanitation and waste management can prevent lead-contaminated refuse or sewage from polluting communities, and it often correlates with effective governance that could extend to controlling industrial pollution. The significant impact of sanitation in our model aligns with the idea that fundamental public health infrastructure reduces exposure pathways \u0026ndash; a finding consistent with literature linking improved water/sanitation services to lower pollutant burdens (Wolf et al. 2023). It is notable that sanitation remains a strong predictor even when accounting for GDP and imports. This implies that how nations invest their wealth (e.g. in public health infrastructure) can offset some detrimental effects of industrialization. In policy terms, bolstering basic environmental health infrastructure may be a tangible near-term strategy to mitigate lead exposure, independent of a country\u0026rsquo;s income level.\u003c/p\u003e\u003cp\u003ePublic awareness of lead hazards, proxied by the Google search index, exhibited a negative coefficient as expected (higher awareness \u003cem\u003epotentially\u003c/em\u003e associated with lower mortality), but this relationship was only marginally significant (p\u0026thinsp;\u0026asymp;\u0026thinsp;0.057) in the SEM. While only marginally significant, we consider this particular result one critical contribution our current study made towards studying lead exposure risks in the Global South. Data scarcity often presents as one of the primary obstacles when conducting environmental health studies in the Global South. Not because the countries do not want to monitor and record environmental health status, but because they simply lack the means to do so. Internet search, while still biased by potential digital divide (Cariolle \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jafar et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) in the Global South, provides a viable alternative to proxy such an important factor in fighting against lead exposure risks.\u003c/p\u003e\u003cp\u003eThe negative sign found in our SEM is encouraging and aligns with the hypothesis that a more informed public will take actions to reduce exposure (for instance, avoiding known sources or pressuring governments for action). The index we used (search volume for \u0026ldquo;lead exposure/poisoning\u0026rdquo;) was adjusted for internet penetration, but we do admit that it still mainly represents the connected, literate segment of society. It may therefore underestimate awareness levels in rural or low-income communities that rely on traditional knowledge channels. Moreover, even if people are aware of lead dangers, they may lack the means to act on that knowledge in the absence of institutional support. This weaker statistical significance might even suggest a synergy: public awareness can only go so far unless accompanied by a framework that enables protective action. Still, the inclusion of this proxy create an opportunity for studies in the near future to take advantage of the power brought by the \u0026ldquo;big data era\u0026rdquo; (Mayer-Sch\u0026ouml;nberger et Cukier 2013).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e5.4. Regulatory Measures and Environmental Context\u003c/h2\u003e\u003cp\u003eSomewhat surprisingly, the lead paint regulation variable was not statistically significant in our model (\u0026ndash;0.018, p\u0026thinsp;=\u0026thinsp;0.93), suggesting no measurable difference in current lead mortality between countries that have instituted lead paint laws and those that have not. This null finding should be interpreted with caution. First, our binary indicator is a crude measure \u0026ndash; it does not capture the strength or enforcement of the regulation, nor how long it has been in effect. Many African nations only recently enacted lead paint limits (often 90 ppm total lead, as recommended globally), and enforcement may lag behind legislation (Muhavani et Mangwiro 2025). It can take years for regulations to translate into public health improvements, especially for a pollutant like lead that persists in the environment. Additionally, countries that adopted regulations might have done so \u003cem\u003ein response\u003c/em\u003e to severe lead problems, meaning the policy\u0026rsquo;s presence could be an indicator of high underlying exposure (a form of reverse causality). In short, the lack of a clear statistical benefit from lead paint laws in the data does not mean such regulations are ineffective; rather, it highlights that simply having a law on the books might not necessarily be sufficient. Effective implementation (industry compliance, public awareness of the law, and active enforcement) is critical for these policies to realize their life-saving potential.\u003c/p\u003e\u003cp\u003eThe NDVI, a proxy for national environmental conditions, also showed no significant association with lead mortality in our model (coefficient \u0026asymp; \u0026minus;\u0026thinsp;0.37, p\u0026thinsp;=\u0026thinsp;0.63). This result indicates that, at a national scale, greenness is probably not a consistent predictor of lead exposure risks. One possible explanation is that the major sources of lethal lead exposure in Africa are largely anthropogenic and localized (industry, mines, batteries, paints, etc.), so broad differences in environmental conditions do not translate into differences in lead exposure caused mortality. While vegetation can reduce airborne dust in specific locales, this effect may be too geographically limited to influence country-level mortality rates. Moreover, in urban slums or industrial hotspots with extreme lead contamination, even high NDVI (parks, trees) might not prevent exposure if, say, children are playing in highly contaminated soils or dust. Conversely, some very arid areas with low NDVI might have little lead pollution if they lack industry (the Sahara has lots of dust but low industrial lead sources). These factors in Africa suggest that NDVI was likely overshadowed by more direct drivers of exposure. We interpret the null result not as evidence that the environment does not matter (vegetation certainly has benefits), but that lead pollution in Africa is driven more by human factors than by natural landscape features.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e5.5. Policy Implications\u003c/h2\u003e\u003cp\u003eAddressing the \u0026ldquo;lead exposure paradox\u0026rdquo; in Africa requires an integrated approach that meshes economic development with proactive environmental health safeguards. Our findings make clear that without intervention, economic growth and globalization can continue to drive up lead exposure \u0026ndash; an outcome that is neither inevitable nor acceptable. The persistence of lead hazards is fundamentally a market failure \u0026ndash; a classic case of socializing costs, privatizing gains: those responsible for pollution (whether industries or traders) often do not bear the full costs, while the health and economic damages fall on the public (externalities), especially on vulnerable groups like children. Correcting this market failure will require a combination of regulatory enforcement and inventive policy tools. Traditional command-and-control regulations \u0026ndash; for example, banning leaded paint, mandating safer battery recycling practices, tightening standards on imports of used electronics \u0026ndash; are essential. These set the rules of the game. Yet, laws on paper must be matched by enforcement capacity and public compliance. Market-based instruments can complement these efforts by shifting incentives: environmental taxes or fees can internalize the cost of lead pollution, and subsidies or loans can encourage businesses to adopt lead-free alternatives and recycling technologies. For instance, a tax on used battery imports could fund proper recycling facilities, or micro-credit could help informal recyclers transition to safer jobs. Innovative financing, including green bonds or international aid targeted at pollution control, could support the up-front costs of cleanup and prevention.\u003c/p\u003e\u003cp\u003eTackling the informal sector is particularly challenging but crucial. In Africa, a large share of lead pollution stems from informal operations \u0026ndash; whether it is backyard smelting of batteries, open-air burning of e-waste, or cottage industries using leaded glazes. Simply outlawing these practices without providing alternatives can backfire, as people depend on them for livelihoods. A more pragmatic strategy is to formalize and incentivize safer practices. This might include establishing collection programs where used batteries can be exchanged for a small payment (to ensure they go to licensed recyclers), offering training and equipment for safer e-waste disassembly, or creating \u0026ldquo;alternative livelihood\u0026rdquo; programs in mining areas so that small-scale miners are not forced to resort to high-risk activities. Such approaches, coupled with remediation of contaminated sites, can gradually reduce legacy lead exposure while sustaining economic well-being. In Africa, the economic burden of lead exposure is estimated at \u003cspan\u003e$\u003c/span\u003e134.7\u0026nbsp;billion per year (about 4% of regional GDP) due to lost lifetime productivity and healthcare expenses (Attina et Trasande 2013). These staggering losses far outweigh the costs of intervention, indicating that lead pollution is not just a health issue but also a significant drag on development.\u003c/p\u003e\u003cp\u003eTo reverse the trend of rising lead harm amid development, African governments and international partners need to prioritize environmental health governance. This means updating and enforcing regulations (e.g. ensuring the new 90 ppm paint standards are adhered to), strengthening institutions that monitor pollution and health (establishing surveillance of blood lead levels, tracking imports of toxic substances), and fostering regional cooperation (Fabolude et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Many lead issues are transboundary. for example, contaminated consumer goods can easily cross borders. Regional blocs like ECOWAS and the African Union have a role to play in harmonizing standards and stopping the shunting of toxic products from one country to another. Public awareness campaigns should be ramped up, leveraging both traditional media and community outreach, since informed communities can demand change and protect themselves better (especially when they are given the tools and legal backing to do so). The recent global phase-out of leaded gasoline \u0026ndash; achieved in 2021 after coordinated effort \u0026ndash; shows that large-scale elimination of a lead source is possible with political will and public pressure. A similar commitment is now needed for the next generation lead threats such as lead in paints, batteries, and e-waste.\u003c/p\u003e\u003cp\u003eOur study underscores a critical insight for African or the entire Global South\u0026rsquo;s sustainable development: economic growth and public health must advance hand in hand. The trade-offs observed between GDP growth and lead mortality are not a reason to stifle development, but a call to steer development onto a sustainable path where increasing prosperity does not come at the cost of poisoning the population. By investing in infrastructure, enforcing safeguards, and innovating policy solutions, Africa can rise out of the development paradox and break the link between development and pollution \u0026ndash; ensuring that the pursuit of prosperity shields rather than sacrifices the health of its people. Such proactive measures are not only morally imperative but economically sound, paving the way for a healthier, more productive future across the continent.\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study investigated the interplay between socioeconomic, environmental, and policy factors and premature mortality attributed to lead exposure across African nations. A key finding is the concerning \u0026ldquo;development paradox,\u0026rdquo; whereby higher national GDP is positively associated with increased lead-related deaths. This paradox underscores a critical gap: economic expansion across Africa frequently outpaces the development and enforcement of necessary environmental health protections. International trade emerges as another influential factor, with greater import volumes, particularly of hazardous materials like e-waste, exacerbating exposure risks. Though intertwined with GDP effects, trade remains a distinct pathway for environmental lead contamination.\u003c/p\u003e\u003cp\u003eInterestingly, interventions like lead paint regulations did not exhibit immediate significant associations with reduced mortality, potentially reflecting implementation challenges, regulatory enforcement issues, and the persistent nature of lead in the environment. The study also introduced a novel, big data-driven proxy for public awareness, Google Trends search volumes, which showed marginal significance. This modest association suggests public awareness alone might be insufficient without enabling regulatory frameworks. The limitations of using internet-based metrics due to Africa\u0026rsquo;s digital divide further highlight the urgent need for innovative and inclusive data collection strategies.\u003c/p\u003e\u003cp\u003eThis pioneer study on integrating big data analytics, remote sensing, spatial data analysis, and environmental health concerns across the African continent is promising to understand lead related premature mortality in developing regions. Still, it is important to acknowledge potential limitations inherent to our ecological study design. At the current stage, the use of aggregated national-level data increases the potential for ecological fallacy, in which observed associations at the country level may not accurately represent individual-level relationships. Unfortunately, the unavailability of data and field work prevents further exploration that we aim for next phase of studies. Additionally, while the spatial error specification is able to provide from a spatial analysis perspective the influence of missed, but spatially autocorrelated factors, residual confounding likely persists such as detailed individual-level socioeconomic status, localized environmental conditions, and the precise extent of enforcement for existing regulations. These limitations should prompt cautious interpretation of our findings and underscore the need for complementary individual-level studies or sub-national analyses to validate and refine these relationships.\u003c/p\u003e\u003cp\u003eAddressing Africa\u0026rsquo;s lead burden effectively requires more than incremental adjustments; it demands a paradigm shift integrating robust environmental health safeguards directly into the core of economic policies. Future strategies must emphasize proactive, rigorously enforced regulations, complemented by innovative market-based instruments. Equally essential is supporting and incentivizing safer practices within Africa\u0026rsquo;s substantial informal sector, as outright bans without alternatives may exacerbate economic hardships. The remarkable economic returns from lead poisoning prevention, estimated at \u003cspan\u003e$\u003c/span\u003e17 to \u003cspan\u003e$\u003c/span\u003e220 for every dollar invested, strongly justify prioritizing these preventive measures.\u003c/p\u003e\u003cp\u003eAchieving significant reductions in lead-related harm will require multi-sectoral cooperation, diverse funding strategies, strengthened governance frameworks, and regional collaboration. In addition, substantial improvements in environmental surveillance and data collection, integrating traditional biomonitoring with emerging big data and remote-sensing technologies, are needed to fill current knowledge gaps. Further research using longitudinal designs, refined measures of regulatory effectiveness and awareness, and granular sub-national analyses will enhance our understanding and inform targeted, effective interventions. Ultimately, decoupling economic development from environmental degradation through comprehensive, integrated strategies is paramount to safeguarding the health and future prosperity of African populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding acknowledgement: The work that provided the basis for this research was supported by funding under an award with the U.S. Department of Housing and Urban Development (grant number NJLTS0027-22). The substance and findings of the work are dedicated to the public. The author and publisher are solely responsible for the accuracy of the statements and interpretations contained in this publication. Such interpretations do not necessarily reflect the views of the Government.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eG.F. and D.Y. wrote the main manuscript text. G.F. and I.A. prepared the data. G.F., C.K. and A.V. compiled the data for final analysis. G.F. and D.Y. conducted the data analyses. D.Y. preapred Figure 1. G.F. prepared Figure 2. G.F., C.K., A.V., and D.Y. prepared Table 1. D.Y. prepared Tables 2 and 3. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be available upone request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbogunrin-Olafisoye, Oladunni B., et Oladayo Adeyi. 2025. \u0026laquo; Environmental and health impacts of unsustainable waste electrical and electronic equipment recycling practices in Nigeria\u0026apos;s informal sector. \u0026raquo; \u003cem\u003eDiscover Chemistry\u003c/em\u003e 2 (1). https://doi.org/10.1007/s44371-024-00075-x. https://dx.doi.org/10.1007/s44371-024-00075-x.\u003c/li\u003e\n\u003cli\u003eAdu Sarfo, Emmanuel, et Rabbi Tweneboah. 2024. \u0026laquo; Mineral wealth paradox: health challenges and environmental risks in African resource-rich areas. \u0026raquo; \u003cem\u003eBMC Public Health\u003c/em\u003e 24 (1): 724. https://doi.org/10.1186/s12889-024-18137-1. https://doi.org/10.1186/s12889-024-18137-1.\u003c/li\u003e\n\u003cli\u003eAttina, Teresa M., et Leonardo Trasande. 2013. \u0026laquo; Economic Costs of Childhood Lead Exposure in Low- and Middle-Income Countries. \u0026raquo; \u003cem\u003eEnvironmental Health Perspectives\u003c/em\u003e 121 (9): 1097-102. https://doi.org/doi:10.1289/ehp.1206424. https://ehp.niehs.nih.gov/doi/abs/10.1289/ehp.1206424.\u003c/li\u003e\n\u003cli\u003eBaggs, Jen. 2009. \u0026laquo; International Trade in Hazardous Waste. \u0026raquo; \u003cem\u003eReview of International Economics\u003c/em\u003e 17 (1): 1-16. https://doi.org/https://doi.org/10.1111/j.1467-9396.2008.00778.x. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9396.2008.00778.x.\u003c/li\u003e\n\u003cli\u003eBede-Ojimadu, Onyinyechi, Cecilia Nwadiuto Amadi, et Orish Ebere Orisakwe. 2018. \u0026laquo; Blood Lead Levels in Women of Child-Bearing Age in Sub-Saharan Africa: A Systematic Review. \u0026raquo; \u003cem\u003eFrontiers in Public Health\u003c/em\u003e 6. https://doi.org/10.3389/fpubh.2018.00367. https://dx.doi.org/10.3389/fpubh.2018.00367.\u003c/li\u003e\n\u003cli\u003eBrunnschweiler, Christa N., Deanna Karapetyan, et P\u0026auml;ivi Lujala. 2024. \u0026laquo; Opportunities and risks of small-scale and artisanal gold mining for local communities: Survey evidence from Ghana. \u0026raquo; \u003cem\u003eThe Extractive Industries and Society\u003c/em\u003e 17: 101403. https://doi.org/10.1016/j.exis.2024.101403. https://dx.doi.org/10.1016/j.exis.2024.101403.\u003c/li\u003e\n\u003cli\u003eCariolle, Jo\u0026euml;l. 2021. \u0026laquo; International connectivity and the digital divide in Sub-Saharan Africa. \u0026raquo; \u003cem\u003eInformation Economics and Policy\u003c/em\u003e 55: 100901. https://doi.org/https://doi.org/10.1016/j.infoecopol.2020.100901. https://www.sciencedirect.com/science/article/pii/S0167624520301451.\u003c/li\u003e\n\u003cli\u003eChen, Wei, Jianing Zhang, Zhaoyuan Yu, et Xiquan Zhao. 2024. \u0026laquo; Structure and evolution of global lead trade network: An industrial chain perspective. \u0026raquo; \u003cem\u003eResources Policy\u003c/em\u003e 90: 104735. https://doi.org/https://doi.org/10.1016/j.resourpol.2024.104735. https://www.sciencedirect.com/science/article/pii/S0301420724001028.\u003c/li\u003e\n\u003cli\u003eCopeland, Brian R. 2008. \u0026laquo; The pollution haven hypothesis. \u0026raquo; \u003cem\u003eHandbook on Trade and the Environment\u003c/em\u003e 2 (7): 60-70.\u003c/li\u003e\n\u003cli\u003eDesye, Belay, Amensisa Hailu Tesfaye, Gete Berihun, Ayechew Ademas, et Birhanu Sewunet. 2023. \u0026laquo; A systematic review of the health effects of lead exposure from electronic waste in children. \u0026raquo; \u003cem\u003eFrontiers in Public Health\u003c/em\u003e 11. https://doi.org/10.3389/fpubh.2023.1113561. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2023.1113561.\u003c/li\u003e\n\u003cli\u003eDignam, Timothy, Rachel B. Kaufmann, Lauren Lestourgeon, et Mary Jean Brown. 2019. \u0026laquo; Control of Lead Sources in the United States, 1970-2017: Public Health Progress and Current Challenges to Eliminating Lead Exposure. \u0026raquo; \u003cem\u003eJournal of Public Health Management and Practice\u003c/em\u003e 25 (1): S13-S22. https://doi.org/10.1097/phh.0000000000000889. https://dx.doi.org/10.1097/PHH.0000000000000889.\u003c/li\u003e\n\u003cli\u003eD\u0026oacute;rea, Jos\u0026eacute; G. 2019. \u0026laquo; Environmental exposure to low-level lead (Pb) co-occurring with other neurotoxicants in early life and neurodevelopment of children. \u0026raquo; \u003cem\u003eEnvironmental Research\u003c/em\u003e 177: 108641. https://doi.org/10.1016/j.envres.2019.108641. https://dx.doi.org/10.1016/j.envres.2019.108641.\u003c/li\u003e\n\u003cli\u003eFabolude, Gift, Charles Knoble, Anvy Vu, et Danlin Yu. 2025. \u0026laquo; Beyond mining: A pioneer attempt to assessing lead exposure risks in Nigeria. \u0026raquo; \u003cem\u003eEnvironmental Impact Assessment Review\u003c/em\u003e 114: 107923. https://doi.org/https://doi.org/10.1016/j.eiar.2025.107923. https://www.sciencedirect.com/science/article/pii/S0195925525001209.\u003c/li\u003e\n\u003cli\u003eForsyth, J. E., K. L. Weaver, K. Maher, M. S. Islam, R. Raqib, M. Rahman, S. Fendorf, et S. P. Luby. 2019. \u0026laquo; Sources of Blood Lead Exposure in Rural Bangladesh. \u0026raquo; \u003cem\u003eEnviron Sci Technol\u003c/em\u003e 53 (19): 11429-36. https://doi.org/10.1021/acs.est.9b00744.\u003c/li\u003e\n\u003cli\u003eFrndak, Seth, Gabriel Barg, Elena I. Queirolo, Nelly Ma\u0026ntilde;ay, Craig Colder, Guan Yu, Zia Ahmed, et Katarzyna Kordas. 2022. \u0026laquo; Do Neighborhood Factors Modify the Effects of Lead Exposure on Child Behavior? \u0026raquo; \u003cem\u003eToxics\u003c/em\u003e 10 (9): 517. https://www.mdpi.com/2305-6304/10/9/517.\u003c/li\u003e\n\u003cli\u003eFu, C., et C. Miller. 2022. \u0026laquo; Using Google Trends as a proxy for occupant behavior to predict building energy consumption. \u0026raquo; \u003cem\u003eApplied Energy\u003c/em\u003e 310: 118343-43. https://doi.org/10.1016/J.APENERGY.2021.118343.\u003c/li\u003e\n\u003cli\u003eGinsberg, Jeremy, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski, et Larry Brilliant. 2009. \u0026laquo; Detecting influenza epidemics using search engine query data. \u0026raquo; \u003cem\u003eNature\u003c/em\u003e 457 (7232): 1012-14. https://doi.org/10.1038/nature07634. https://doi.org/10.1038/nature07634.\u003c/li\u003e\n\u003cli\u003eGould, E. 2009. \u0026laquo; Childhood lead poisoning: conservative estimates of the social and economic benefits of lead hazard control. \u0026raquo; \u003cem\u003eEnviron Health Perspect\u003c/em\u003e 117 (7): 1162-7. https://doi.org/10.1289/ehp.0800408.\u003c/li\u003e\n\u003cli\u003eJafar, Zain, Jonathan D. Quick, Eszter Rim\u0026aacute;nyi, et Godfrey Musuka. 2024. \u0026laquo; Social Media and Digital Inequity: Reducing Health Inequities by Closing the Digital Divide. \u0026raquo; \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e 21 (11): 1420. https://www.mdpi.com/1660-4601/21/11/1420.\u003c/li\u003e\n\u003cli\u003eKnipe, Duleeka, David Gunnell, Hannah Evans, Ann John, et Daisy Fancourt. 2021. \u0026laquo; Is Google Trends a useful tool for tracking mental and social distress during a public health emergency? A time\u0026ndash;series analysis. \u0026raquo; \u003cem\u003eJournal of Affective Disorders\u003c/em\u003e 294: 737-44. https://doi.org/10.1016/j.jad.2021.06.086. https://linkinghub.elsevier.com/retrieve/pii/S0165032721006741.\u003c/li\u003e\n\u003cli\u003eKnoble, Charles, Gift Fabolude, Anvy Vu, et Danlin Yu. 2024. \u0026laquo; From crisis to prevention: mining big data for public health insights during the flint water crisis. \u0026raquo; \u003cem\u003eDiscover Sustainability\u003c/em\u003e 5 (1): 289. https://doi.org/10.1007/s43621-024-00514-w. https://doi.org/10.1007/s43621-024-00514-w.\u003c/li\u003e\n\u003cli\u003eKordas, Katarzyna, Julia Ravenscroft, Ying Cao, et Elena V. McLean. 2018. \u0026laquo; Lead Exposure in Low and Middle-Income Countries: Perspectives and Lessons on Patterns, Injustices, Economics, and Politics. \u0026raquo; \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e 15 (11): 2351. https://doi.org/10.3390/ijerph15112351. https://dx.doi.org/10.3390/ijerph15112351.\u003c/li\u003e\n\u003cli\u003eKupzig, Nina, Jenny Kupzig, et Johanna Meier. 2024. \u0026laquo; An augmented Environmental Kuznets Curve and a new measure of water pollution: an investigation of water pollution in Africa. \u0026raquo; \u003cem\u003eDiscover Water\u003c/em\u003e 4 (1): 122. https://doi.org/10.1007/s43832-024-00177-8. https://doi.org/10.1007/s43832-024-00177-8.\u003c/li\u003e\n\u003cli\u003eLanphear, B. P., R. Hornung, J. Khoury, K. Yolton, P. Baghurst, D. C. Bellinger, R. L. Canfield, K. N. Dietrich, R. Bornschein, T. Greene, S. J. Rothenberg, H. L. Needleman, L. Schnaas, G. Wasserman, J. Graziano, et R. Roberts. 2005. \u0026laquo; Low-level environmental lead exposure and children\u0026apos;s intellectual function: an international pooled analysis. \u0026raquo; \u003cem\u003eEnviron Health Perspect\u003c/em\u003e 113 (7): 894-9. https://doi.org/10.1289/ehp.7688.\u003c/li\u003e\n\u003cli\u003eLebbie, Tamba S., Omosehin D. Moyebi, Kwadwo Ansong Asante, Julius Fobil, Marie Noel Brune-Drisse, William A. Suk, Peter D. Sly, Julia Gorman, et David O. Carpenter. 2021. \u0026laquo; E-Waste in Africa: A Serious Threat to the Health of Children. \u0026raquo; \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e 18 (16): 8488. https://doi.org/10.3390/ijerph18168488. https://dx.doi.org/10.3390/ijerph18168488.\u003c/li\u003e\n\u003cli\u003eLevin, Ronnie. 2016. \u0026laquo; The attributable annual health costs of U.S. occupational lead poisoning. \u0026raquo; \u003cem\u003eInternational Journal of Occupational and Environmental Health\u003c/em\u003e 22 (2): 107-20. https://doi.org/10.1080/10773525.2016.1173945. https://doi.org/10.1080/10773525.2016.1173945.\u003c/li\u003e\n\u003cli\u003eLo, Yi-Chun, Carrie A. Dooyema, Antonio Neri, James Durant, Taran Jefferies, Andrew Medina-Marino, Lori De Ravello, Douglas Thoroughman, Lora Davis, Raymond S. Dankoli, Matthias Y. Samson, Luka M. Ibrahim, Ossai Okechukwu, Nasir T. Umar-Tsafe, Alhassan H. Dama, et Mary Jean Brown. 2012. \u0026laquo; Childhood Lead Poisoning Associated with Gold Ore Processing: a Village-Level Investigation\u0026mdash;Zamfara State, Nigeria, October\u0026ndash;November 2010. \u0026raquo; \u003cem\u003eEnvironmental Health Perspectives\u003c/em\u003e 120 (10): 1450-55. https://doi.org/10.1289/ehp.1104793. https://dx.doi.org/10.1289/ehp.1104793.\u003c/li\u003e\n\u003cli\u003eMathee, A. 2014. \u0026laquo; Towards the prevention of lead exposure in South Africa: contemporary and emerging challenges. \u0026raquo; \u003cem\u003eNeurotoxicology\u003c/em\u003e 45: 220-3. https://doi.org/10.1016/j.neuro.2014.07.007.\u003c/li\u003e\n\u003cli\u003eMathee, A., N. Naicker, T. Kootbodien, T. Mahuma, P. Nkomo, I. Naik, et T. de Wet. 2014. \u0026laquo; A cross-sectional analytical study of geophagia practices and blood metal concentrations in pregnant women in Johannesburg, South Africa. \u0026raquo; \u003cem\u003eS Afr Med J\u003c/em\u003e 104 (8): 568-73. https://doi.org/10.7196/samj.7466.\u003c/li\u003e\n\u003cli\u003eMathee, Angela, Halina R\u0026ouml;llin, Yasmin Von Schirnding, Jonathan Levin, et Ina Naik. 2006. \u0026laquo; Reductions in blood lead levels among school children following the introduction of unleaded petrol in South Africa. \u0026raquo; \u003cem\u003eEnvironmental Research\u003c/em\u003e 100 (3): 319-22. https://doi.org/10.1016/j.envres.2005.08.001. https://dx.doi.org/10.1016/j.envres.2005.08.001.\u003c/li\u003e\n\u003cli\u003eMayer-Sch\u0026ouml;nberger, Viktor, et Kenneth Cukier. 2013. \u003cem\u003eBig data: A revolution that will transform how we live, work, and think\u003c/em\u003e. Houghton Mifflin Harcourt.\u003c/li\u003e\n\u003cli\u003eMoya-Alvarez, Violeta, Michael Osei Mireku, Pierre Ayotte, Michel Cot, et Florence Bodeau-Livinec. 2016. \u0026laquo; Elevated Blood Lead Levels Are Associated with Reduced Risk of Malaria in Beninese Infants. \u0026raquo; \u003cem\u003ePLOS ONE\u003c/em\u003e 11 (2): e0149049. https://doi.org/10.1371/journal.pone.0149049. https://dx.doi.org/10.1371/journal.pone.0149049.\u003c/li\u003e\n\u003cli\u003eMuhavani, Ang\u0026eacute;lique Umutesi, et Alexander Mangwiro. 2025. \u0026laquo; Africa: Lead Poisoning - the Persistent Threat From Mismanaged Waste. \u0026raquo;. United Nations Environment Programme (UNEP). Consult\u0026eacute; le 5/14/2025. https://allafrica.com/stories/202501130438.html.\u003c/li\u003e\n\u003cli\u003eNriagu, Jerome O., Mary L. Blankson, et Kwamena Ocran. 1996. \u0026laquo; Childhood lead poisoning in Africa: a growing public health problem. \u0026raquo; \u003cem\u003eScience of The Total Environment\u003c/em\u003e 181 (2): 93-100. https://doi.org/10.1016/0048-9697(95)04954-1. https://dx.doi.org/10.1016/0048-9697(95)04954-1.\u003c/li\u003e\n\u003cli\u003eOndayo, Maureene Auma, Michael J. Watts, Clive J. Mitchell, David C. P. King, et Odipo Osano. 2024. \u0026laquo; Review: Artisanal Gold Mining in Africa\u0026mdash;Environmental Pollution and Human Health Implications. \u0026raquo; \u003cem\u003eExposure and Health\u003c/em\u003e 16 (4): 1067-95. https://doi.org/10.1007/s12403-023-00611-7. https://dx.doi.org/10.1007/s12403-023-00611-7.\u003c/li\u003e\n\u003cli\u003eOulhote, Youssef. 2015. \u0026laquo; Lead Exposure In African Countries: State Of The Art, Challenges And Perspectives. \u0026raquo; \u003cem\u003eISEE Conference Abstracts\u003c/em\u003e 2015 (1): 2173. https://doi.org/10.1289/isee.2015.2015-2173. https://dx.doi.org/10.1289/isee.2015.2015-2173.\u003c/li\u003e\n\u003cli\u003ePascale, Antonio, Adriana Sosa, Cristina Bares, Alejandra Battocletti, Mar\u0026iacute;a Jos\u0026eacute; Moll, Dar\u0026iacute;o Pose, Amalia Laborde, Hugo Gonz\u0026aacute;lez, et Gabriella Feola. 2016. \u0026laquo; E-Waste Informal Recycling: An Emerging Source of Lead Exposure in South America. \u0026raquo; \u003cem\u003eAnnals of Global Health\u003c/em\u003e 82 (1): 197. https://doi.org/10.1016/j.aogh.2016.01.016. https://dx.doi.org/10.1016/j.aogh.2016.01.016.\u003c/li\u003e\n\u003cli\u003ePennington, Audrey F., Madison R. Smith, Stella O. Chuke, Cheryl R. Cornwell, Paul B. Allwood, et Joseph G. Courtney. 2024. \u0026laquo; Effects of Blood Lead Levels \u0026amp;lt;10 \u0026micro;g/dL in School-Age Children and Adolescents: A Scoping Review. \u0026raquo; \u003cem\u003ePediatrics\u003c/em\u003e 154 (Supplement 2). https://doi.org/10.1542/peds.2024-067808F. https://doi.org/10.1542/peds.2024-067808F.\u003c/li\u003e\n\u003cli\u003ePoudel, Kritika, Atsuko Ikeda, Hisanori Fukunaga, Marie-Noel Brune Drisse, Lesley Jayne Onyon, Julia Gorman, Amalia Laborde, et Reiko Kishi. 2024. \u0026laquo; How does formal and informal industry contribute to lead exposure? A narrative review from Vietnam, Uruguay, and Malaysia. \u0026raquo; \u003cem\u003eReviews on Environmental Health\u003c/em\u003e 39 (2): 371-88. https://doi.org/10.1515/reveh-2022-0224. https://dx.doi.org/10.1515/reveh-2022-0224.\u003c/li\u003e\n\u003cli\u003eP\u0026uuml;schel, P., K. M. Agbeko, A. A. Amoabeng-Nti, J. Arko-Mensah, J. Bertram, J. N. Fobil, S. Waldschmidt, K. L\u0026ouml;hndorf, T. Schettgen, M. Lakemeyer, A. Morrison, et T. K\u0026uuml;pper. 2024. \u0026laquo; Lead exposure by E-waste disposal and recycling in Agbogbloshie, Ghana. \u0026raquo; \u003cem\u003eInternational Journal of Hygiene and Environmental Health\u003c/em\u003e 259: 114375. https://doi.org/10.1016/j.ijheh.2024.114375. https://dx.doi.org/10.1016/j.ijheh.2024.114375.\u003c/li\u003e\n\u003cli\u003eSAMRC. 2019. \u0026laquo; SAMRC supports global ban on lead in paint. \u0026raquo;. https://www.samrc.ac.za/press-releases/samrc-supports-global-ban-lead-paint.\u003c/li\u003e\n\u003cli\u003eSelendy, J.M.H. 2011. \u003cem\u003eWater and Sanitation-Related Diseases and the Environment: Challenges, Interventions, and Preventive Measures\u003c/em\u003e. Wiley.\u003c/li\u003e\n\u003cli\u003eSuk, William A., Hamid Ahanchian, Kwadwo Ansong Asante, David O. Carpenter, Fernando Diaz-Barriga, Eun-Hee Ha, Xia Huo, Malcolm King, Mathuros Ruchirawat, Emerson R. Da Silva, Leith Sly, Peter D. Sly, Renato T. Stein, Martin Van Den Berg, Heather Zar, et Philip J. Landrigan. 2016. \u0026laquo; Environmental Pollution: An Under-recognized Threat to Children\u0026rsquo;s Health, Especially in Low- and Middle-Income Countries. \u0026raquo; \u003cem\u003eEnvironmental Health Perspectives\u003c/em\u003e 124 (3). https://doi.org/10.1289/ehp.1510517. https://dx.doi.org/10.1289/ehp.1510517.\u003c/li\u003e\n\u003cli\u003eThompson, Joshua J., Robert L. Wilby, Tom Matthews, et Conor Murphy. 2022. \u0026laquo; The utility of Google Trends as a tool for evaluating flooding in data‐scarce places. \u0026raquo; \u003cem\u003eArea\u003c/em\u003e 54 (2): 203-12. https://doi.org/10.1111/area.12719. https://dx.doi.org/10.1111/area.12719.\u003c/li\u003e\n\u003cli\u003eTirima, Simba, Casey Bartrem, Ian Von Lindern, Margrit Von Braun, Douglas Lind, Shehu Mohammed Anka, et Aishat Abdullahi. 2016. \u0026laquo; Environmental Remediation to Address Childhood Lead Poisoning Epidemic due to Artisanal Gold Mining in Zamfara, Nigeria. \u0026raquo; \u003cem\u003eEnvironmental Health Perspectives\u003c/em\u003e 124 (9): 1471-78. https://doi.org/10.1289/ehp.1510145. https://dx.doi.org/10.1289/ehp.1510145.\u003c/li\u003e\n\u003cli\u003eUNEP. 2015. \u0026laquo; Governments Agree to Set Legal Limits on Lead in Paint in Africa. \u0026raquo;. https://www.unep.org/news-and-stories/story/governments-agree-set-legal-limits-lead-paint-africa.\u003c/li\u003e\n\u003cli\u003e--. 2017. \u0026laquo; Turning tragedies into opportunities: Overcoming Africa\u0026rsquo;s lead challenge. \u0026raquo;. https://www.unep.org/news-and-stories/story/turning-tragedies-opportunities-overcoming-africas-lead-challenge.\u003c/li\u003e\n\u003cli\u003e--. 2020. \u0026laquo; Update on the Global Status of Legal Limits on Lead in Paint December 2020. \u0026raquo;. https://saicmknowledge.org/.\u003c/li\u003e\n\u003cli\u003e--. 2021. \u0026laquo; Era of leaded petrol over, eliminating a major threat to human and planetary health. \u0026raquo;. https://www.unep.org/news-and-stories/press-release/era-leaded-petrol-over-eliminating-major-threat-human-and-planetary.\u003c/li\u003e\n\u003cli\u003eVorvolakos, Th, S. Arseniou, et M. Samakouri. 2016. \u0026laquo; There is no safe threshold for lead exposure: \u0026Alpha; literature review. \u0026raquo; \u003cem\u003ePsychiatriki\u003c/em\u003e 27 (3): 204-14. https://doi.org/10.22365/jpsych.2016.273.204. https://dx.doi.org/10.22365/jpsych.2016.273.204.\u003c/li\u003e\n\u003cli\u003eWolf, J., R. B. Johnston, A. Ambelu, B. F. Arnold, R. Bain, M. Brauer, J. Brown, B. A. Caruso, T. Clasen, J. M. Colford, Jr., J. E. Mills, B. Evans, M. C. Freeman, B. Gordon, G. Kang, C. F. Lanata, K. O. Medlicott, A. Pr\u0026uuml;ss-Ust\u0026uuml;n, C. Troeger, S. Boisson, et O. Cumming. 2023. \u0026laquo; Burden of disease attributable to unsafe drinking water, sanitation, and hygiene in domestic settings: a global analysis for selected adverse health outcomes. \u0026raquo; \u003cem\u003eLancet\u003c/em\u003e 401 (10393): 2060-71. https://doi.org/10.1016/s0140-6736(23)00458-0.\u003c/li\u003e\n\u003cli\u003eYu, D. 2025a. \u003cem\u003eSpatial Data Analysis With R\u003c/em\u003e. SAGE Publications.\u003c/li\u003e\n\u003cli\u003eYu, D.L., et Y. D. Wei. 2008. \u0026laquo; Spatial data analysis of regional development in Greater Beijing, China, in a GIS environment. \u0026raquo; \u003cem\u003ePapers in Regional Science\u003c/em\u003e 87 (1): 97-117. https://doi.org/10.1111/j.1435-5957.2007.00148.x. \u003cu\u003e\u0026lt;Go to ISI\u0026gt;://WOS:000254735000006\u003c/u\u003e.\u003c/li\u003e\n\u003cli\u003eYu, Danlin. 2025b. \u0026laquo; Lead exposure in the 21st century: Modeling a path from crisis to prevention. \u0026raquo; \u003cem\u003eEco-Environment \u0026amp; Health\u003c/em\u003e: 100159. https://doi.org/https://doi.org/10.1016/j.eehl.2025.100159. https://www.sciencedirect.com/science/article/pii/S2772985025000286.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Lead Exposure, Environmental Health, Africa, Development Paradox, Mortality, Public Health Policy","lastPublishedDoi":"10.21203/rs.3.rs-7161842/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7161842/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLead exposure remains a serious environmental health threat in Africa, especially for children. Amid economic growth, weak regulatory safeguards may exacerbate lead-related morbidity and mortality \u0026ndash; the development paradox. This study investigates how socioeconomic development, infrastructure, and policy factors relate to premature deaths from lead exposure across African countries. We analyzed data for 52 African nations on premature deaths attributed to lead exposure (Global Burden of Disease 2021), alongside indicators including GDP, lead paint bans, public awareness (Google search index), vegetation cover (NDVI), import volume, and sanitation access. A multivariate log-linear regression assessed associations with lead-attributable mortality. Residual spatial autocorrelation is detected, and a spatial error model accounted for unobserved geographic effects. Guided by Environmental‑Justice and Pollution‑Haven theory, we test three propositions: (i) GDP\u0026ndash;mortality coupling, (ii) trade‑mediated toxicity transfer, and (iii) infrastructure‑driven mitigation. The model finds that higher GDP was significantly associated with increased lead mortality (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:=0.557,\\:p\u0026lt;0.001\\)\u003c/span\u003e\u003c/span\u003e), as was import volume (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:=0.342,\\:p=0.008\\)\u003c/span\u003e\u003c/span\u003e). Improved sanitation correlated with lower mortality (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:=-0.019,\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\u0026lt;0.001\\)\u003c/span\u003e\u003c/span\u003e). Public awareness showed a marginally significant protective effect (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p=0.057\\)\u003c/span\u003e\u003c/span\u003e). Lead paint regulation and vegetation cover were not significantly associated. The spatial error model improved fit and identified spatially correlated risks (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:\\approx\\:0.50,\\:p\u0026lt;0.001\\)\u003c/span\u003e\u003c/span\u003e). Our study for the first time suggests that in Africa, economic development without environmental safeguards may elevate lead exposure \u0026ndash; a \u0026ldquo;lead exposure paradox.\u0026rdquo; Globalization facilitates hazardous imports (e.g., e-waste), compounding risks. Basic infrastructure like sanitation appears protective. These findings call for integrated industrial, trade and health policies aligned with Sustainable Development Goals (SDGs) 3, 8 and 12.\u003c/p\u003e","manuscriptTitle":"Development, Trade and Environmental Justice: Decoupling Economic Growth from Lead Mortality in Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 14:50:15","doi":"10.21203/rs.3.rs-7161842/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-13T03:51:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273437036825216065298448520732353401768","date":"2026-04-02T14:45:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108147800635709931676910527463869361623","date":"2026-01-31T17:52:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-07T10:06:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302188810949536392986651327636025067011","date":"2025-11-05T01:18:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270054055884525635885449327762524029831","date":"2025-11-04T09:21:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-04T02:46:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-14T07:28:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-07T04:21:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-07-19T04:48:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"604d2f0c-28a1-472d-a0fa-264236e24b35","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":57672826,"name":"Earth and environmental sciences/Environmental sciences"},{"id":57672827,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":57672828,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-11-13T14:50:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 14:50:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7161842","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7161842","identity":"rs-7161842","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}