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.

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.

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.

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.

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.

Predicting the risk of GenX contamination in private well water using a machine-learned Bayesian network model.

Per- and polyfluoroalkyl substances (PFAS) are emerging contaminants that pose significant challenges in mechanistic fate and transport modeling due to their diverse and complex chemical characteristics. Machine learning provides a novel approach for predicting the spatial distribution of PFAS in the environment. We used spatial location information to link PFAS measurements from 1207 private drinking water wells around a fluorochemical manufacturing facility to a mechanistic model of PFAS air deposition and to publicly available data on soil, land use, topography, weather, and proximity to multiple PFAS sources. We used the resulting linked data set to train a Bayesian network model to predict the risk that GenX, a member of the PFAS class, would exceed a state provisional health goal (140 ng/L) in private well water. The model had high accuracy (ROC curve index for five-fold cross-validation of 0.85, 90% CI 0.84-0.87). Among factors significantly associated with GenX risk in private wells, the most important was the historic rate of atmospheric deposition of GenX from the fluorochemical manufacturing facility. The model output was used to generate spatial risk predictions for the study area to aid in risk assessment, environmental investigations, and targeted public health interventions.

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.

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.

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.

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.

Protecting wastewater workers from disease risks: Personal protective equipment guidelines.

The 2013-2016 Ebola epidemic revived concerns about infection risks to wastewater workers. Prior research has shown that wastewater can contain a variety of known and emerging pathogens and that wastewater workers are at increased risk of infectious illnesses. However, guidelines on using personal protective equipment (PPE) to decrease these risks are lacking. We engaged 34 wastewater utility personnel and public health experts to conduct a job safety analysis identifying tasks in which workers could be exposed to pathogens and to develop a PPE selection matrix for preventing those exposures. We identified 43 relevant job tasks. Recommended PPE ranges from durable gloves (all tasks) to safety glasses (24 tasks), Tyvek suits or coveralls (4 tasks), and respiratory protection (N95 mask or face mask, depending on the activity, 10 tasks). The PPE selection matrix can serve as a guide for protecting the 120,000 wastewater workers in the United States from known and emerging pathogens. PRACTITIONER POINTS: Wastewater workers are at increased risk of infectious illnesses. Policies to protect wastewater workers from these illnesses are lacking. We developed guidelines for use of personal protective equipment by wastewater workers to prevent exposure to infectious agents.

Well water testing in African-American communities without municipal infrastructure: Beliefs driving decisions.

Some peri-urban African-American communities in North Carolina remain excluded from nearby municipal water service, forcing them to rely on unregulated private wells. Despite evidence of elevated drinking water contamination risks in these communities, water monitoring is rare. To identify factors influencing decisions to test private wells, we developed and administered a survey to residents of affected areas. A factor analysis identified three constructs significantly associated with a decreased likelihood of water testing: (1) the misconception that contaminants can be detected by sensory perception, (2) concerns about costs of testing and/or water treatment, and (3) not knowing how to get a water test or having time to do so. Increased knowledge about how to test and the importance of testing was significantly associated with a decreased concern about costs which, in turn, was significantly associated with an increased odds of testing. These results suggest the need for targeted risk communications that correct the misperception that contaminants can be tasted, smelled, or seen. The results also suggest the need for clear information about how to get a water test and for low-cost testing programs. Increased monitoring could empower residents to take protective actions and potentially mobilize political support for water service extensions.

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.

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.

Advancing Dose-Response Assessment Methods for Environmental Regulatory Impact Analysis: A Bayesian Belief Network Approach Applied to Inorganic Arsenic.

Dose-response functions used in regulatory risk assessment are based on studies of whole organisms and fail to incorporate genetic and metabolomic data. Bayesian belief networks (BBNs) could provide a powerful framework for incorporating such data, but no prior research has examined this possibility. To address this gap, we develop a BBN-based model predicting birthweight at gestational age from arsenic exposure via drinking water and maternal metabolic indicators using a cohort of 200 pregnant women from an arsenic-endemic region of Mexico. We compare BBN predictions to those of prevailing slope-factor and reference-dose approaches. The BBN outperforms prevailing approaches in balancing false-positive and false-negative rates. Whereas the slope-factor approach had 2% sensitivity and 99% specificity and the reference-dose approach had 100% sensitivity and 0% specificity, the BBN's sensitivity and specificity were 71% and 30%, respectively. BBNs offer a promising opportunity to advance health risk assessment by incorporating modern genetic and metabolomic data.

Estimating Active Transportation Behaviors to Support Health Impact Assessment in the United States.

Health impact assessment (HIA) has been promoted as a means to encourage transportation and city planners to incorporate health considerations into their decision-making. Ideally, HIAs would include quantitative estimates of the population health effects of alternative planning scenarios, such as scenarios with and without infrastructure to support walking and cycling. However, the lack of baseline estimates of time spent walking or biking for transportation (together known as "active transportation"), which are critically related to health, often prevents planners from developing such quantitative estimates. To address this gap, we use data from the 2009 US National Household Travel Survey to develop a statistical model that estimates baseline time spent walking and biking as a function of the type of transportation used to commute to work along with demographic and built environment variables. We validate the model using survey data from the Raleigh-Durham-Chapel Hill, NC, USA, metropolitan area. We illustrate how the validated model could be used to support transportation-related HIAs by estimating the potential health benefits of built environment modifications that support walking and cycling. Our statistical model estimates that on average, individuals who commute on foot spend an additional 19.8 (95% CI 16.9-23.2) minutes per day walking compared to automobile commuters. Public transit riders walk an additional 5.0 (95% CI 3.5-6.4) minutes per day compared to automobile commuters. Bicycle commuters cycle for an additional 28.0 (95% CI 17.5-38.1) minutes per day compared to automobile commuters. The statistical model was able to predict observed transportation physical activity in the Raleigh-Durham-Chapel Hill region to within 0.5 MET-hours per day (equivalent to about 9 min of daily walking time) for 83% of observations. Across the Raleigh-Durham-Chapel Hill region, an estimated 38 (95% CI 15-59) premature deaths potentially could be avoided if the entire population walked 37.4 min per week for transportation (the amount of transportation walking observed in previous US studies of walkable neighborhoods). The approach developed here is useful both for estimating baseline behaviors in transportation HIAs and for comparing the magnitude of risks associated with physical inactivity to other competing health risks in urban areas.

Reducing Emergency Department Visits for Acute Gastrointestinal Illnesses in North Carolina (USA) by Extending Community Water Service.

BACKGROUND: Previous analyses have suggested that unregulated private drinking water wells carry a higher risk of exposure to microbial contamination than regulated community water systems. In North Carolina, ~35% of the state's population relies on private wells, but the health impact associated with widespread reliance on such unregulated drinking water sources is unknown. OBJECTIVES: We estimated the total number of emergency department visits for acute gastrointestinal illness (AGI) attributable to microbial contamination in private wells in North Carolina per year, the costs of those visits, and the potential health benefits of extending regulated water service to households currently relying on private wells for their drinking water. METHODS: We developed a population intervention model using 2007-2013 data from all 122 North Carolina emergency departments along with microbial contamination data for all 2,120 community water systems and for 16,138 private well water samples collected since 2008. RESULTS: An estimated 29,400 (95% CI: 26,600, 32,200) emergency department visits per year for acute gastrointestinal illness were attributable to microbial contamination in drinking water, constituting approximately 7.3% (95% CI: 6.6, 7.9%) of all AGI-related visits. Of these attributable cases, 99% (29,200; 95% CI: 26,500, 31,900) were associated with private well contamination. The estimated statewide annual cost of emergency department visits attributable to microbiological contamination of drinking water is 40.2 million USD (95% CI: 2.58 million USD, 193 million USD), of which 39.9 million USD (95% CI: 2.56 million USD, 192 million USD) is estimated to arise from private well contamination. An estimated 2,920 (95% CI: 2,650, 3,190) annual emergency department visits could be prevented by extending community water service to 10% of the population currently relying on private wells. CONCLUSIONS: This research provides new evidence that extending regulated community water service to populations currently relying on private wells may decrease the population burden of acute gastrointestinal illness. CITATION: DeFelice NB, Johnston JE, Gibson JM. 2016. Reducing emergency department visits for acute gastrointestinal illnesses in North Carolina (USA) by extending community water service. Environ Health Perspect 124:1583-1591; http://dx.doi.org/10.1289/EHP160.

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.