Early life lead exposure from private well water increases juvenile delinquency risk among US teens.

Early life exposure to environmental lead (Pb) has been linked to decreased IQ, behavior problems, lower lifetime earnings, and increased criminal activity. Beginning in the 1970s, limits on Pb in paint, gasoline, food cans, and regulated water utilities sharply curtailed US environmental Pb exposure. Nonetheless, hundreds of thousands of US children remain at risk. This study reports on how unregulated private well water is an underrecognized Pb exposure source that is associated with an increased risk of teenage juvenile delinquency. We build a longitudinal dataset linking blood Pb measurements for 13,580 children under age 6 to their drinking water source, individual- and neighborhood-level demographics, and reported juvenile delinquency records. We estimate how early life Pb exposure from private well water influences reported delinquency. On average, children in homes with unregulated private wells had 11% higher blood Pb than those with community water service. This higher blood Pb was significantly associated with reported delinquency. Compared to children with community water service, those relying on private wells had a 21% (95% CI: 5 to 40%) higher risk of being reported for any delinquency and a 38% (95% CI: 10 to 73%) increased risk of being reported for serious delinquency after age 14. These results suggest that there could be substantial but as-yet-unrecognized social benefits from intervention programs to prevent children's exposure to Pb from private wells, on which 13% of the US population relies.

Effect of Community Water Service on Lead in Drinking Water in an Environmental Justice Community.

Multiple recent studies have found elevated lead (Pb) concentrations in tap water in U.S. homes relying on unregulated private wells. The main Pb source is dissolution from household plumbing, fixtures, and well components. Here, we leverage a natural experiment and citizen science approach to evaluate how extending community water service to an environmental justice community relying on private wells affects Pb in household water. We analyzed Pb in 260 first-draw kitchen tap water samples collected by individual homeowners over a 5-month period in residences that did and did not connect to the community system. Before the community water system was extended, 25% of homes had Pb > 15 µg/L (the U.S. regulatory action level for community water systems) in first-draw water samples. Pb was significantly correlated with nickel (ρ = 0.61), zinc (ρ = 0.50), and copper (ρ = 0.40), suggesting that corrosion of brass fittings and fixtures is the main Pb source. Among homes that connected to the community system, Pb decreased rapidly and was sustained at levels well below 15 µg/L over the study period. Overall, connecting to the municipal water supply was associated with a 92.5% decrease in first-draw tap water Pb.

Research gaps and priorities for quantitative microbial risk assessment (QMRA).

The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose-response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co-exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source-to-outcome continuum. Cross-cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies.

Predicting Groundwater PFOA Exposure Risks with Bayesian Networks: Empirical Impact of Data Preprocessing on Model Performance.

The plethora of data on PFASs in environmental samples collected in response to growing concern about these chemicals could enable the training of machine-learning models for predicting exposure risks. However, differences in sampling and analysis methods across data sets must be reconciled through data preprocessing, and little information is available about how such manipulations affect the resulting models. This study evaluates how data preprocessing influences machine-learned Bayesian network models of PFOA in groundwater. We link 19 years of PFOA measurements from Minnesota, USA, to publicly available information about potential PFOA sources and factors that may influence their environmental fate. Nine different preprocessing methods were tested, and the resulting data sets were used to train models to predict the probability of PFOA ≥ 35 ppt, the 2017 Minnesota health advisory level. Different preprocessing approaches produced varying model structures with significantly different accuracies. Nonetheless, models showed similar relationships between predictor variables and PFOA exposure risks, and all models were relatively accurate, distinguishing wells at high risk from those at low risk for 82.0% to 89.0% of test data samples. There was a trade-off between data quality and model performance since a stricter data screening strategy decreased the sample size for model training.

Children drinking private well water have higher blood lead than those with city water.

Although the Flint, Michigan, water crisis renewed concerns about lead (Pb) in city drinking water, little attention has been paid to Pb in private wells, which provide drinking water for 13% of the US population. This study evaluates the risk of Pb exposure in children in households relying on private wells. It is based on a curated dataset of blood Pb records from 59,483 North Carolina children matched with household water source information. We analyze the dataset for statistical associations between children's blood Pb and household drinking water source. The analysis shows that children in homes relying on private wells have 25% increased odds (95% CI 6.2 to 48%, P < 0.01) of elevated blood Pb, compared with children in houses served by a community water system that is regulated under the Safe Drinking Water Act. This increased Pb exposure is likely a result of corrosion of household plumbing and well components, because homes relying on private wells rarely treat their water to prevent corrosion. In contrast, corrosion control is required in regulated community water systems. These findings highlight the need for targeted outreach to prevent Pb exposure for the 42.5 million Americans depending on private wells for their drinking water.

Lead Distribution in Urban Soil in a Medium-Sized City: Household-Scale Analysis.

This study characterizes potential soil lead (Pb) exposure risk at the household scale in Greensboro, North Carolina, using an innovative combination of field sampling, statistical analysis, and machine-learning techniques. Soil samples were collected at the dripline, yard, and street side at 462 households (total sample size = 2310). Samples were analyzed for Pb and then combined with publicly available data on potential historic Pb sources, soil properties, and household and neighborhood demographic characteristics. This curated data set was then analyzed with statistical and machine-learning techniques to identify the drivers of potential soil Pb exposure risks and to build predictive models. Among all samples, 43% exceeded current guidelines for Pb in residential gardens. There were significant racial disparities in potential soil Pb exposure risk; soil Pb at the dripline increased by 19% for every 25% increase in the neighborhood population identifying as Black. A machine-learned Bayesian network model was able to classify residential parcels by risk of exceeding residential gardening standards with excellent reproducibility in cross validation. These findings underscore the need for targeted outreach programs to prevent Pb exposure in residential areas and demonstrate an approach for prioritizing outreach locations.

Lead in drinking water at North Carolina childcare centers: Piloting a citizen science-based testing strategy.

BACKGROUND: Drinking water is a lingering hazard in the effort to eliminate childhood exposure to lead (Pb), a neurotoxin that affects cognitive and behavioral development. This study characterized Pb in municipal drinking water at North Carolina, US, childcare centers. The study also demonstrates a scalable, citizen science-based drinking water testing strategy for Pb at childcare centers. METHODS: Licensed childcare centers in four North Carolina counties were recruited. One administrator per center completed a survey and was trained to collect first-draw drinking water samples in their center. Samples were shipped with pre-paid labels for laboratory analysis using inductively coupled plasma mass spectrometry. Multilevel logistic regression and Bayesian network analysis were used to identify factors associated with a risk of exceeding the 1 µg/L American Academy of Pediatrics reference level and the US Environmental Protection Agency (US EPA) 15 µg/L treatment-based action level. Results were provided to centers along with risk mitigation recommendations. RESULTS: Of 103 enrolled centers, 86 completed the study, submitting 1,266 drinking water samples in total. Approximately 77% of drinking water samples contained detectable Pb (≥0.1 µg/L), and 97% of centers had at least one drinking water sample with detectable Pb. More than 63% of centers had at least one drinking water sample with >1 µg/L Pb, and 17% of centers had at least one drinking water sample with Pb above 15 µg/L. There was high variability in Pb concentrations at water points within the same center. DISCUSSION: This study demonstrated a high prevalence and variability of Pb in first-draw samples of drinking water at childcare centers in North Carolina, US. Results underscore the importance of testing for Pb at every tap used for drinking and cooking in childcare centers. The use of employees as citizen scientists is a feasible strategy to identify Pb in specific drinking water taps.

Risk of Antibiotic-Resistant Staphylococcus aureus Dispersion from Hog Farms: A Critical Review.

The World Health Organization has declared antibiotic resistance "one of the biggest threats to global health." Mounting evidence suggests that antibiotic use in industrial-scale hog farming is contributing to the spread of antibiotic-resistant Staphylococcus aureus. To capture available evidence on these risks, we searched peer-reviewed studies published before June 2017 and conducted a meta-analysis of these studies' estimates of the prevalence of swine-associated, antibiotic-resistant S. aureus in animals, humans, and the environment. The 166 relevant studies revealed consistent evidence of livestock-associated methicillin-resistant S. aureus (MRSA) in hog herds (55.3%) raised with antibiotics. MRSA prevalence was also substantial in slaughterhouse pigs (30.4%), industrial hog operation workers (24.4%), and veterinarians (16.8%). The prevalence of swine-associated, multidrug-resistant S. aureus (MDRSA)-with resistance to three or more antibiotics-is not as well documented. Nonetheless, sufficient studies were available to estimate MDRSA pooled prevalence in conventional hog operation workers (15.0%), workers' household members (13.0%), and community members (5.37%). Evidence also suggests that antibiotic-resistant S. aureus can be present in air, soil, water, and household surface samples gathered in or near high-intensity hog operations. An important caveat is that prevalence estimates for humans reflect colonization, not active infection, and the health risks of colonization remain poorly understood. In addition, these pooled results may not represent risks in specific locations, due to wide geographic variation. Nonetheless, these results underscore the need for additional preventive action to stem the spread of antibiotic-resistant pathogens from livestock operations and a streamlined reporting system to track this risk.

A Bayesian Belief Network Model Assessing the Risk to Wastewater Workers of Contracting Ebola Virus Disease During an Outbreak.

During an outbreak of Ebola virus disease (EVD), hospitals' connections to municipal wastewater systems may provide a path for patient waste bearing infectious viral particles to pass from the hospital into the wastewater treatment system, potentially posing risks to sewer and wastewater workers. To quantify these risks, we developed a Bayesian belief network model incorporating data on virus behavior and survival along with structural characteristics of hospitals and wastewater treatment systems. We applied the model to assess risks under several different scenarios of workers' exposure to wastewater for a wastewater system typical of a mid-sized U.S. city. The model calculates a median daily risk of developing EVD of approximately 6.1×10-12 (90% confidence interval: 1.0×10-12 to 5.4×10-9 ; mean 1.8×10-6 ) when no prior exposure conditions are specified. Under a worst-case scenario in which a worker stationed in the sewer adjacent to the hospital accidentally ingests several drops (0.35 mL) of wastewater, median risk is 5.8×10-4 (90% CI: 8.8×10-7 to 9.5×10-2 ; mean 3.2×10-2 ) . Disinfection of patient waste with peracetic acid for 15 minutes prior to flushing decreases the estimated median risk to 3.8×10-7 (90% CI: 4.1×10-9 to 8.6×10-5 ; mean 2.9×10-5 ). The results suggest that requiring hospitals to disinfect EVD patient waste prior to flushing may be advisable. The modeling framework can provide insight into managing patient waste during future outbreaks of highly virulent infectious pathogens.

Safeguarding Children's Health: Time to Enact a Health-Based Standard and Comprehensive Testing, Mitigation, and Communication Protocol for Lead in Drinking Water.

Lead was a known toxin before the Roman Empire, yet exposure remains a public health concern today. Although there is no safe lead exposure level, a health-based drinking water standard has not been established. The Clean Water for Carolina Kids Study highlights the need for a health-based standard.

Disparities in Water and Sewer Services in North Carolina: An Analysis of the Decision-Making Process.

OBJECTIVES: We examined the factors that affect access to municipal water and sewer service for unincorporated communities relying on wells and septic tanks. METHODS: Using a multisite case study design, we conducted in-depth, semistructured interviews with 25 key informants from 3 unincorporated communities in Hoke, New Hanover, and Transylvania counties, North Carolina, July through September 2013. Interviewees included elected officials, health officials, utility providers, and community members. We coded the interviews in ATLAS.ti to identify common themes. RESULTS: Financing for water and sewer service emerged as the predominant factor that influenced decisions to extend these services. Improved health emerged as a minor factor, suggesting that local officials may not place a high emphasis on the health benefits of extending public water and sewer services. Awareness of failed septic systems in communities can prompt city officials to extend sewer service to these areas; however, failed systems are often underreported. CONCLUSIONS: Understanding the health costs and benefits of water and sewer extension and integrating these findings into the local decision-making process may help address disparities in access to municipal services.

Acute Gastrointestinal Illness Risks in North Carolina Community Water Systems: A Methodological Comparison.

The magnitude and spatial variability of acute gastrointestinal illness (AGI) cases attributable to microbial contamination of U.S. community drinking water systems are not well characterized. We compared three approaches (drinking water attributable risk, quantitative microbial risk assessment, and population intervention model) to estimate the annual number of emergency department visits for AGI attributable to microorganisms in North Carolina community water systems. All three methods used 2007-2013 water monitoring and emergency department data obtained from state agencies. The drinking water attributable risk method, which was the basis for previous U.S. Environmental Protection Agency national risk assessments, estimated that 7.9% of annual emergency department visits for AGI are attributable to microbial contamination of community water systems. However, the other methods' estimates were more than 2 orders of magnitude lower, each attributing 0.047% of annual emergency department visits for AGI to community water system contamination. The differences in results between the drinking water attributable risk method, which has been the main basis for previous national risk estimates, and the other two approaches highlight the need to improve methods for estimating endemic waterborne disease risks, in order to prioritize investments to improve community drinking water systems.

The Effects of Urban Form on Ambient Air Pollution and Public Health Risk: A Case Study in Raleigh, North Carolina.

Since motor vehicles are a major air pollution source, urban designs that decrease private automobile use could improve air quality and decrease air pollution health risks. Yet, the relationships among urban form, air quality, and health are complex and not fully understood. To explore these relationships, we model the effects of three alternative development scenarios on annual average fine particulate matter (PM2.5 ) concentrations in ambient air and associated health risks from PM2.5 exposure in North Carolina's Raleigh-Durham-Chapel Hill area. We integrate transportation demand, land-use regression, and health risk assessment models to predict air quality and health impacts for three development scenarios: current conditions, compact development, and sprawling development. Compact development slightly decreases (-0.2%) point estimates of regional annual average PM2.5 concentrations, while sprawling development slightly increases (+1%) concentrations. However, point estimates of health impacts are in opposite directions: compact development increases (+39%) and sprawling development decreases (-33%) PM2.5-attributable mortality. Furthermore, compactness increases local variation in PM2.5 concentrations and increases the severity of local air pollution hotspots. Hence, this research suggests that while compact development may improve air quality from a regional perspective, it may also increase the concentration of PM2.5 in local hotspots and increase population exposure to PM2.5 . Health effects may be magnified if compact neighborhoods and PM2.5 hotspots are spatially co-located. We conclude that compactness alone is an insufficient means of reducing the public health impacts of transportation emissions in automobile-dependent regions. Rather, additional measures are needed to decrease automobile dependence and the health risks of transportation emissions.

Health and air quality benefits of policies to reduce coal-fired power plant emissions: a case study in North Carolina.

We analyzed sulfur dioxide (SO2) emissions and fine particulate sulfate (PM2.5 sulfate) concentrations in the southeastern United States during 2002-2012, in order to evaluate the health impacts in North Carolina (NC) of the NC Clean Smokestacks Act of 2002. This state law required progressive reductions (beyond those mandated by federal rules) in pollutant emissions from NC's coal-fired power plants. Although coal-fired power plants remain NC's leading SO2 source, a trend analysis shows significant declines in SO2 emissions (-20.3%/year) and PM2.5 sulfate concentrations (-8.7%/year) since passage of the act. Emissions reductions were significantly greater in NC than in neighboring states, and emissions and PM2.5 sulfate concentration reductions were highest in NC's piedmont region, where 9 of the state's 14 major coal-fired power plants are located. Our risk model estimates that these air quality improvements decreased the risk of premature death attributable to PM2.5 sulfate in NC by about 63%, resulting in an estimated 1700 (95% CI: 1500, 1800) deaths prevented in 2012. These findings lend support to recent studies predicting that implementing the proposed federal Cross-State Air Pollution Rule (recently upheld by the U.S. Supreme Court) could substantially decrease U.S. premature deaths attributable to coal-fired power plant emissions.

Updating exposure models of indoor air pollution due to vapor intrusion: Bayesian calibration of the Johnson-Ettinger model.

The migration of chlorinated volatile organic compounds from groundwater to indoor air--known as vapor intrusion--is an important exposure pathway at sites with contaminated groundwater. High-quality screening methods to prioritize homes for monitoring and remediation are needed, because measuring indoor air quality in privately owned buildings is often logistically and financially infeasible. We demonstrate an approach for improving the accuracy of the Johnson-Ettinger model (JEM), which the Environmental Protection Agency (EPA) recommends as a screening tool in assessing vapor intrusion risks. We use Bayesian statistical techniques to update key Johnson-Ettinger input parameters, and we compare the performance of the prior and updated models in predicting indoor air concentrations measured in 20 homes. Overall, the updated model reduces the root mean squared error in the predicted concentration by 66%, in comparison to the prior model. Further, in 18 of the 20 homes, the mean measured concentration is within the 90% confidence interval of the concentration predicted by the updated model. The resulting calibrated model accounts for model uncertainty and variability and decreases the false negatives rate; hence, it may offer an improved screening approach, compared to the current EPA deterministic approach.

Screening houses for vapor intrusion risks: a multiple regression analysis approach.

The migration of chlorinated volatile organic compounds from groundwater to indoor air-known as vapor intrusion-can be an important exposure pathway at hazardous waste sites. Because sampling indoor air at every potentially affected home is often logistically infeasible, screening tools are needed to help identify at-risk homes. Currently, the U.S. Environmental Protection Agency (EPA) uses a simple screening approach that employs a generic vapor "attenuation factor," the ratio of the indoor air pollutant concentration to the pollutant concentration in the soil gas directly above the groundwater table. At every potentially affected home above contaminated groundwater, the EPA assumes the vapor attenuation factor is less than 1/1000--that is, that the indoor air concentration will not exceed 1/1000 times the soil-gas concentration immediately above groundwater. This paper reports on a screening-level model that improves on the EPA approach by considering environmental, contaminant, and household characteristics. The model is based on an analysis of the EPA's vapor intrusion database, which contains almost 2,400 indoor air and corresponding subsurface concentration samples collected in 15 states. We use the site data to develop a multilevel regression model for predicting the vapor attenuation factor. We find that the attenuation factor varies significantly with soil type, depth to groundwater, season, household foundation type, and contaminant molecular weight. The resulting model decreases the rate of false negatives compared to EPA's screening approach.

Probabilistic analysis showing that a combination of Bacteroides and Methanobrevibacter source tracking markers is effective for identifying waters contaminated by human fecal pollution.

Microbial source tracking assays to identify sources of waterborne contamination typically target genetic markers of host-specific microorganisms. However, no bacterial marker has been shown to be 100% host-specific, and cross-reactivity has been noted in studies evaluating known source samples. Using 485 challenge samples from 20 different human and animal fecal sources, this study evaluated microbial source tracking markers including the Bacteroides HF183 16S rRNA, M. smithii nifH, and Enterococcus esp gene targets that have been proposed as potential indicators of human fecal contamination. Bayes' Theorem was used to calculate the conditional probability that these markers or a combination of markers can correctly identify human sources of fecal pollution. All three human-associated markers were detected in 100% of the sewage samples analyzed. Bacteroides HF183 was the most effective marker for determining whether contamination was specifically from a human source, and greater than 98% certainty that contamination was from a human source was shown when both Bacteroides HF183 and M. smithii nifH markers were present. A high degree of certainty was attained even in cases where the prior probability of human fecal contamination was as low as 8.5%. The combination of Bacteroides HF183 and M. smithii nifH source tracking markers can help identify surface waters impacted by human fecal contamination, information useful for prioritizing restoration activities or assessing health risks from exposure to contaminated waters.

Burden of disease from contaminated drinking water in countries with high access to safely managed water: A systematic review.

The vast majority of residents of high-income countries (≥90%) reportedly have high access to safely managed drinking water. Owing perhaps to the widely held perception of near universal access to high-quality water services in these countries, the burden of waterborne disease in these contexts is understudied. This systematic review aimed to: identify population-scale estimates of waterborne disease in countries with high access to safely managed drinking water, compare methods to quantify disease burden, and identify gaps in available burden estimates. We conducted a systematic review of population-scale disease burden estimates attributed to drinking water in countries where ≥90% of the population has access to safely managed drinking water per official United Nations monitoring. We identified 24 studies reporting estimates for disease burden attributable to microbial contaminants. Across these studies, the median burden of gastrointestinal illness risks attributed to drinking water was ∼2,720 annual cases per 100,000 population. Beyond exposure to infectious agents, we identified 10 studies reporting disease burden-predominantly, cancer risks-associated with chemical contaminants. Across these studies, the median excess cancer cases attributable to drinking water was 1.2 annual cancer cases per 100,000 population. These median estimates slightly exceed WHO-recommended normative targets for disease burden attributable to drinking water and these results highlight that there remains important preventable disease burden in these contexts, particularly among marginalized populations. However, the available literature was scant and limited in geographic scope, disease outcomes, range of microbial and chemical contaminants, and inclusion of subpopulations (rural, low-income communities; Indigenous or Aboriginal peoples; and populations marginalized due to discrimination by race, ethnicity, or socioeconomic status) that could most benefit from water infrastructure investments. Studies quantifying drinking water-associated disease burden in countries with reportedly high access to safe drinking water, focusing on specific subpopulations lacking access to safe water supplies and promoting environmental justice, are needed.