Schools and Wastewater Surveillance: Practical Implications for an Emerging Technology to Impact Child Health.

Since the start of the COVID-19 pandemic, wastewater surveillance has emerged as a public health tool that supplements traditional surveillance methods used to detect the prevalence of the SARS-CoV-2 virus in communities. In May 2020, the Houston Health Department (HHD) partnered with a coalition of municipal and academic partners to develop a wastewater monitoring and reporting system for the city of Houston, Texas. The HHD subsequently launched a program to conduct targeted wastewater sampling at 52 school sites located in a large, urban school district in Houston. Data generated by this program are shared with school district officials and nurses from participating schools. Although initial feedback from these stakeholders indicated that they considered the wastewater data valuable, the emergency nature of the pandemic prevented a systematic evaluation of the program. To address this gap in knowledge, the HHD and Rice University conducted a study to determine how wastewater data are used to make decisions about COVID-19 prevention and mitigation practices in schools. Our findings indicate that maximizing the utility of wastewater data in the school context will require the development of communication strategies and education efforts tailored to the needs of specific audiences and improving collaboration between local health departments, school districts, and school nurses.

Population-Based Estimates of SARS-CoV-2 Seroprevalence in Houston, Texas as of September 2020.

BACKGROUND: In contrast to studies that relied on volunteers or convenience sampling, there are few population-based severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence investigations and most were conducted early in the pandemic. The health department of the fourth largest US city recognized that sound estimates of viral impact were needed to inform decision making. METHODS: Adapting standardized disaster research methodology, in September 2020 the city was divided into high and low strata based on reverse-transcriptase polymerase chain reaction (RT-PCR) positivity rates; census block groups within each stratum were randomly selected with probability proportional to size, followed by random selection of households within each group. Using 2 immunoassays, the proportion of infected individuals was estimated for the city, by positivity rate and sociodemographic and other characteristics. The degree of underascertainment of seroprevalence was estimated based on RT-PCR-positive cases. RESULTS: Seroprevalence was estimated to be 14% with near 2-fold difference in areas with high (18%) versus low (10%) RT-PCR positivity rates and was 4 times higher compared to case-based surveillance data. CONCLUSIONS: Seroprevalence was higher than previously reported and greater than estimated from RT-PCR data. Results will be used to inform public health decisions about testing, outreach, and vaccine rollout.

Metal air pollution partnership solutions: building an academic-government-community-industry collaboration to improve air quality and health in environmental justice communities in Houston.

BACKGROUND: From 2006 to 2011, the City of Houston received nearly 200 community complaints about air pollution coming from some metal recycling facilities. The investigation by the Houston Health Department (HHD) found that while operating within legal limits, emissions from facilities that use torch cutting, a technique generating metal aerosols, may increase health risks for neighboring residents. Choosing to use collaborative problem solving over legislative rulemaking, HHD reached out to The University of Texas Health Science Center at Houston (UTHealth) to further evaluate and develop plans to mitigate, if necessary, health risks associated with metal emissions from these facilities. METHODS: Utilizing a community-based participatory research approach, we constituted a research team from academia, HHD and an air quality advocacy group and a Community Advisory Board (CAB) to draw diverse stakeholders (i.e., frustrated and concerned residents and wary facility managers acting within their legal rights) into an equitable, trusting and respectful space to work together. Next, we investigated metal air pollution and inhalation health risks of adults living near metal recyclers and ascertained community views about environmental health using key informant interviews, focus groups and surveys. Finally, working collaboratively with the CAB, we developed neighborhood-specific public health action plans to address research findings. RESULTS: After overcoming challenges, the CAB evolved into an effective partnership with greater trust, goodwill, representation and power among members. Working together to translate and share health risk assessment results increased accessibility of the information. These results, coupled to community survey findings, set the groundwork for developing and implementing a stakeholder-informed action plan, which included a voluntary framework to reduce metal emissions in the scrap yard, improved lines of communication and environmental health leadership training. Tangible outcomes of enhanced capacity of our community and governmental partners included trained residents to conduct door-to-door surveys, adaptation of our field training protocol and survey by our community partner and development of a successful HHD program to engage residents to improve environmental health in their neighborhood. CONCLUSIONS: Academic-government-community-industry partnerships can reduce environmental health disparities in underserved neighborhoods near industrial facilities.

Feeding butter with elevated content of trans-10, cis-12 conjugated linoleic acid to obese-prone rats impairs glucose and insulin tolerance.

BACKGROUND: We recently demonstrated that feeding a natural CLAt10,c12-enriched butter to lean female rats resulted in small, but significant increases in fasting glucose and insulin concentrations, and impaired insulin tolerance. Our goal was to extend these findings by utilizing the diabetes-prone female fatty Zucker rat. Rats were fed custom diets containing 45 % kcal of fat derived from control and CLAt10,c12-enriched butter for 8 weeks. METHODS: CLA t10,c12-enriched butter was prepared from milk collected from cows fed a high fermentable carbohydrate diet to create subacute rumen acidosis (SARA); control (non-SARA) butter was collected from cows fed a low grain diet. Female fatty Zucker rats (10 weeks old) were randomly assigned to one of four diet treatments: i) low fat (10 % kcal), ii) 45 % kcal lard, iii) 45 % kcal SARA butter, or iv) 45 % kcal non-SARA butter. A low fat fed lean Zucker group was used as a control group. After 8 weeks, i) glucose and insulin tolerance tests, ii) insulin signaling in muscle, adipose and liver, and iii) metabolic caging measurements were performed. RESULTS: Glucose and insulin tolerance were significantly impaired in all fatty Zucker groups, but to the greatest extent in the LARD and SARA conditions. Insulin signaling (AKT phosphorylation) was impaired in muscle, visceral (perigonadal) adipose tissue and liver in fatty Zucker rats, but was generally similar across dietary groups. Physical activity, oxygen consumption, food intake and weight gain were also similar amongst the various fatty Zucker groups. CONCLUSIONS: Increasing the consumption of a food naturally enriched with CLAt10,c12 significantly worsens glucose and insulin tolerance in a diabetes-prone rodent model. This outcome is not explained by changes in tissue insulin signaling, physical activity, energy expenditure, food intake or body mass.

Feeding butter with elevated content of trans-10, cis-12 conjugated linoleic acid to lean rats does not impair glucose tolerance or muscle insulin response.

BACKGROUND: Numerous studies have investigated the effects of isolated CLA supplementation on glucose homeostasis in humans and rodents. However, both the amount and relative abundance of CLA isomers in supplemental form are not representative of what is consumed from natural sources. No study to date has examined the effects of altered CLA isomer content within a natural food source. Our goal was to increase the content of the insulin desensitizing CLAt10,c12 isomer relative to the CLAc9,t11 isomer in cow's milk by inducing subacute rumenal acidosis (SARA), and subsequently investigate the effects of this milk fat on parameters related to glucose and insulin tolerance in rats. METHODS: We fed female rats (~2.5 to 3 months of age) CLA t10,c12 -enriched (SARA) butter or non-SARA butter based diets for 4 weeks in either low (10% of kcal from fat; 0.18% total CLA by weight) or high (60% of kcal from fat; 0.55% total CLA by weight) amounts. In an effort to extend these findings, we then fed rats high (60% kcal) amounts of SARA or non-SARA butter for a longer duration (8 weeks) and assessed changes in whole body glucose, insulin and pyruvate tolerance in comparison to low fat and 60% lard conditions. RESULTS: There was a main effect for increased fasting blood glucose and insulin in SARA vs. non-SARA butter groups after 4 weeks of feeding (p < 0.05). However, blood glucose and insulin concentration, and maximal insulin-stimulated glucose uptake in skeletal muscle were similar in all groups. Following 8 weeks of feeding, insulin tolerance was impaired by the SARA butter, but not glucose or pyruvate tolerance. The non-SARA butter did not impair tolerance to glucose, insulin or pyruvate. CONCLUSIONS: This study suggests that increasing the consumption of a naturally enriched CLAt10,c12 source, at least in rats, has minimal impact on whole body glucose tolerance or muscle specific insulin response.

Development and demonstration of a data visualization platform of short-term guidelines for ambient air levels of benzene during disaster response in Houston, Texas.

Industrial disasters have caused hazardous air pollution and public health impacts. Response officials have developed limited exposure guidelines to direct them during the event; often, guidelines are outdated and may not represent relevant elevated-exposure periods. The 2019 Intercontinental Terminals Company (ITC) fire in Houston, Texas led to large-scale releases of benzene and presented a public health threat. This incident highlights the need for effective response and nimble, rapid public health communication. We developed a data-driven visualization tool to store, display, and interpret ambient benzene concentrations to assist health officials during environmental emergencies. Guidance values to interpret risk from acute exposure to benzene were updated using recent literature that also considers exposure periodicity. The visualization platform can process data from different sampling instruments and air monitors automatically, and displays information publicly in real time, along with the associated risk information and action recommendations. The protocol was validated by applying it retrospectively to the ITC event. The new guidance values are 6-30 times lower than those derived by the Texas regulatory agency. Fixed-site monitoring data, assessed using the protocol and revised thresholds, indicated that eight shelter-in-place and 17 air-quality alerts may have been considered. At least one of these shelter-in-place alerts corresponded to prolonged, elevated benzene concentrations (~1000 ppb). This new tool addresses essential gaps in the timely communication of air pollution measurements, provides context to understand potential health risks from exposure to benzene, and provides a clear protocol for local officials in responding to industrial air releases of benzene. Integr Environ Assess Manag 2024;20:533-546. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

A Novel Framework for Internal Responses to Detection of Pathogens in Wastewater by Public Health Agencies.

OBJECTIVES: To build on the success of wastewater surveillance during the COVID-19 pandemic, jurisdictions funded under the Centers for Disease Control and Prevention National Wastewater Surveillance System are looking to expand their wastewater programs to detect more pathogens. However, many public health agencies do not know how to use the collected wastewater data to formulate public health responses, underscoring a need for guidance. To address this knowledge gap, the Houston Health Department (HHD) developed a novel response framework that outlines an internal action plan that is tailored by pathogen type after detection of various pathogens in wastewater. MATERIALS AND METHODS: In July 2023, HHD met with subject matter experts (eg, bureau chiefs, program managers) in internal departments, including epidemiology, immunization, and health education, to discuss the general outline of the response framework and each department's anticipated role after pathogen detection. RESULTS: The internal framework established a flow for notifications and the actions to be taken by departments in HHD, with the goals of (1) ensuring timely and efficient responses to pathogen detections, (2) creating accountability within departments for taking their assigned actions, and (3) making certain that HHD was prepared for intervention implementation when a new pathogen was detected. PRACTICE IMPLICATIONS: As more public health agencies expand their wastewater surveillance programs to target additional pathogens, development of internal action plans tailored to departmental capacity and programs is an important step for public health agencies. The information compiled in this response framework can be a model for other public health agencies to adopt when expanding the scope of their wastewater monitoring systems.

PreK-12 school and citywide wastewater monitoring of the enteric viruses astrovirus, rotavirus, and sapovirus.

Wastewater monitoring is an efficient and effective way to surveil for various pathogens in communities. This is especially beneficial in areas of high transmission, such as preK-12 schools, where infections may otherwise go unreported. In this work, we apply wastewater disease surveillance using school and community wastewater from across Houston, Texas to monitor three major enteric viruses: astrovirus, sapovirus genogroup GI, and group A rotavirus. We present the results of a 10-week study that included the analysis of 164 wastewater samples for astrovirus, rotavirus, and sapovirus in 10 preK-12 schools, 6 wastewater treatment plants, and 2 lift stations using newly designed RT-ddPCR assays. We show that the RT-ddPCR assays were able to detect astrovirus, rotavirus, and sapovirus in school, lift station, and wastewater treatment plant (WWTP) wastewater, and that a positive detection of a virus in a school sample was paired with a positive detection of the same virus at a downstream lift station or wastewater treatment plant over 97 % of the time. Additionally, we show how wastewater detections of rotavirus in schools and WWTPs were significantly associated with citywide viral intestinal infections. School wastewater can play a role in the monitoring of enteric viruses and in the detection of outbreaks, potentially allowing public health officials to quickly implement mitigation strategies to prevent viral spread into surrounding communities.

Online trend estimation and detection of trend deviations in sub-sewershed time series of SARS-CoV-2 RNA measured in wastewater.

Wastewater surveillance has proven a cost-effective key public health tool to understand a wide range of community health diseases and has been a strong source of information on community levels and spread for health departments throughout the SARS- CoV-2 pandemic. Studies spanning the globe demonstrate the strong association between virus levels observed in wastewater and quality clinical case information of the population served by the sewershed. Few of these studies incorporate the temporal dependence present in sampling over time, which can lead to estimation issues which in turn impact conclusions. We contribute to the literature for this important public health science by putting forward time series methods coupled with statistical process control that (1) capture the evolving trend of a disease in the population; (2) separate the uncertainty in the population disease trend from the uncertainty due to sampling and measurement; and (3) support comparison of sub-sewershed population disease dynamics with those of the population represented by the larger downstream treatment plant. Our statistical methods incorporate the fact that measurements are over time, ensuring correct statistical conclusions. We provide a retrospective example of how sub-sewersheds virus levels compare to the upstream wastewater treatment plant virus levels. An on-line algorithm supports real-time statistical assessment of deviations of virus level in a population represented by a sub-sewershed to the virus level in the corresponding larger downstream wastewater treatment plant. This information supports public health decisions by spotlighting segments of the population where outbreaks may be occurring.

Citywide wastewater SARS-CoV-2 levels strongly correlated with multiple disease surveillance indicators and outcomes over three COVID-19 waves.

Public health surveillance systems for COVID-19 are multifaceted and include multiple indicators reflective of different aspects of the burden and spread of the disease in a community. With the emergence of wastewater disease surveillance as a powerful tool to track infection dynamics of SARS-CoV-2, there is a need to integrate and validate wastewater information with existing disease surveillance systems and demonstrate how it can be used as a routine surveillance tool. A first step toward integration is showing how it relates to other disease surveillance indicators and outcomes, such as case positivity rates, syndromic surveillance data, and hospital bed use rates. Here, we present an 86-week long surveillance study that covers three major COVID-19 surges. City-wide SARS-CoV-2 RNA viral loads in wastewater were measured across 39 wastewater treatment plants and compared to other disease metrics for the city of Houston, TX. We show that wastewater levels are strongly correlated with positivity rate, syndromic surveillance rates of COVID-19 visits, and COVID-19-related general bed use rates at hospitals. We show that the relative timing of wastewater relative to each indicator shifted across the pandemic, likely due to a multitude of factors including testing availability, health-seeking behavior, and changes in viral variants. Next, we show that individual WWTPs led city-wide changes in SARS-CoV-2 viral loads, indicating a distributed monitoring system could be used to enhance the early-warning capability of a wastewater monitoring system. Finally, we describe how the results were used in real-time to inform public health response and resource allocation.

Enabling accurate and early detection of recently emerged SARS-CoV-2 variants of concern in wastewater.

As clinical testing declines, wastewater monitoring can provide crucial surveillance on the emergence of SARS-CoV-2 variant of concerns (VoCs) in communities. In this paper we present QuaID, a novel bioinformatics tool for VoC detection based on quasi-unique mutations. The benefits of QuaID are three-fold: (i) provides up to 3-week earlier VoC detection, (ii) accurate VoC detection (>95% precision on simulated benchmarks), and (iii) leverages all mutational signatures (including insertions & deletions).

Data to Action: Community-Based Participatory Research to Address Concerns about Metal Air Pollution in Overburdened Neighborhoods near Metal Recycling Facilities in Houston.

BACKGROUND: Exposures to environmental contaminants can be influenced by social determinants of health. As a result, persons living in socially disadvantaged communities may experience disproportionate health risks from environmental exposures. Mixed methods research can be used to understand community-level and individual-level exposures to chemical and nonchemical stressors contributing to environmental health disparities. Furthermore, community-based participatory research (CBPR) approaches can lead to more effective interventions. OBJECTIVES: We applied mixed methods to identify environmental health perceptions and needs among metal recyclers and residents living in disadvantaged neighborhoods near metal recycling facilities in Houston, Texas, in a CBPR study, Metal Air Pollution Partnership Solutions (MAPPS). Informed by what we learned and our previous findings from cancer and noncancer risk assessments of metal air pollution in these neighborhoods, we developed an action plan to lower metal aerosol emissions from metal recycling facilities and enhance community capacity to address environmental health risks. METHODS: Key informant interviews, focus groups, and community surveys were used to identify environmental health concerns of residents. A diverse group from academia, an environmental justice advocacy group, the community, the metal recycling industry, and the local health department collaborated and translated these findings, along with results from our prior risk assessments, to inform a multifaceted public health action plan. RESULTS: An evidence-based approach was used to develop and implement neighborhood-specific action plans. Plans included a voluntary framework of technical and administrative controls to reduce metal emissions in the metal recycling facilities, direct lines of communication among residents, metal recyclers, and local health department officials, and environmental health leadership training. DISCUSSION: Using a CBPR approach, health risk assessment findings based on outdoor air monitoring campaigns and community survey results informed a multipronged environmental health action plan to mitigate health risks associated with metal air pollution. https://doi.org/10.1289/EHP11405.

Public Health Interventions Guided by Houston's Wastewater Surveillance Program During the COVID-19 Pandemic.

Since the start of the COVID-19 pandemic, wastewater surveillance has emerged as a powerful tool used by public health authorities to track SARS-CoV-2 infections in communities. In May 2020, the Houston Health Department began working with a coalition of municipal and academic partners to develop a wastewater monitoring and reporting system for the city of Houston, Texas. Data collected from the system are integrated with other COVID-19 surveillance data and communicated through different channels to local authorities and the general public. This information is used to shape policies and inform actions to mitigate and prevent the spread of COVID-19 at municipal, institutional, and individual levels. Based on the success of this monitoring and reporting system to drive public health protection efforts, the wastewater surveillance program is likely to become a standard part of the public health toolkit for responding to infectious diseases and, potentially, other disease-causing outbreaks.

Wastewater surveillance of SARS-CoV-2 and influenza in preK-12 schools shows school, community, and citywide infections.

Wastewater surveillance is a passive and efficient way to monitor the spread of infectious diseases in large populations and high transmission areas such as preK-12 schools. Infections caused by respiratory viruses in school-aged children are likely underreported, particularly because many children may be asymptomatic or mildly symptomatic. Wastewater monitoring of SARS-CoV-2 has been studied extensively and primarily by sampling at centralized wastewater treatment plants, and there are limited studies on SARS-CoV-2 in preK-12 school wastewater. Similarly, wastewater detections of influenza have only been reported in wastewater treatment plant and university manhole samples. Here, we present the results of a 17-month wastewater monitoring program for SARS-CoV-2 (n = 2176 samples) and influenza A and B (n = 1217 samples) in 51 preK-12 schools. We show that school wastewater concentrations of SARS-CoV-2 RNA were strongly associated with COVID-19 cases in schools and community positivity rates, and that influenza detections in school wastewater were significantly associated with citywide influenza diagnosis rates. Results were communicated back to schools and local communities to enable mitigation strategies to stop the spread, and direct resources such as testing and vaccination clinics. This study demonstrates that school wastewater surveillance is reflective of local infections at several population levels and plays a crucial role in the detection and mitigation of outbreaks.

QuaID: Enabling Earlier Detection of Recently Emerged SARS-CoV-2 Variants of Concern in Wastewater.

As clinical testing declines, wastewater monitoring can provide crucial surveillance on the emergence of SARS-CoV-2 variants of concern (VoC) in communities. Multiple recent studies support that wastewater-based SARS-CoV-2 detection of circulating VoC can precede clinical cases by up to two weeks. Furthermore, wastewater based epidemiology enables wide population-based screening and study of viral evolutionary dynamics. However, highly sensitive detection of emerging variants remains a complex task due to the pooled nature of environmental samples and genetic material degradation. In this paper we propose quasi-unique mutations for VoC identification, implemented in a novel bioinformatics tool (QuaID) for VoC detection based on quasi-unique mutations. The benefits of QuaID are three-fold: (i) provides up to 3 week earlier VoC detection compared to existing approaches, (ii) enables more sensitive VoC detection, which is shown to be tolerant of >50% mutation drop-out, and (iii) leverages all mutational signatures, including insertions & deletions.

Direct comparison of RT-ddPCR and targeted amplicon sequencing for SARS-CoV-2 mutation monitoring in wastewater.

Over the course of the COVID-19 pandemic, variants of SARS-CoV-2 have emerged that are more contagious and more likely to cause breakthrough infections. Targeted amplicon sequencing approach is a gold standard for identification and analysis of variants. However, when applied to environmental samples such as wastewater, it remains unclear how sensitive this method is for detecting variant-associated mutations in environmental samples. Here we directly compare a targeted amplicon sequencing approach (using ARTIC v3; hereafter referred to as sequencing) with RT-ddPCR quantification for the detection of five mutations that are characteristic of variants of concern (VoCs) in wastewater samples. In total, 547 wastewater samples were analyzed using both methods in parallel. When we observed positive mutation detections by RT-ddPCR, 42.6% of the detection events were missed by sequencing, due to negative detection or the limited read coverage at the mutation position. Further, when sequencing reported negative or depth-limited mutation detections, 26.7% of those events were instead positive detections by RT-ddPCR, highlighting the relatively poor sensitivity of sequencing. No or weak associations were observed between quantitative measurements of target mutations determined by RT-ddPCR and sequencing. These findings caution the use of quantitative measurements of SARS-CoV-2 variants in wastewater samples determined solely based on sequencing.

Responding to Natural and Industrial Disasters: Partnerships and Lessons Learned.

OBJECTIVES: The aim of this study was to provide insights learned from disaster research response (DR2) efforts following Hurricane Harvey in 2017 to launch DR2 activities following the Intercontinental Terminals Company (ITC) fire in Deer Park, Texas, in 2019. METHODS: A multidisciplinary group of academic, community, and government partners launched a myriad of DR2 activities. RESULTS: The DR2 response to Hurricane Harvey focused on enhancing environmental health literacy around clean-up efforts, measuring environmental contaminants in soil and water in impacted neighborhoods, and launching studies to evaluate the health impact of the disaster. The lessons learned after Harvey enabled rapid DR2 activities following the ITC fire, including air monitoring and administering surveys and in-depth interviews with affected residents. CONCLUSIONS: Embedding DR2 activities at academic institutions can enable rapid deployment of lessons learned from one disaster to enhance the response to subsequent disasters, even when those disasters are different. Our experience demonstrates the importance of academic institutions working with governmental and community partners to support timely disaster response efforts. Efforts enabled by such experience include providing health and safety training and consistent and reliable messaging, collecting time-sensitive and critical data in the wake of the event, and launching research to understand health impacts and improve resiliency.

The Texas flood registry: a flexible tool for environmental and public health practitioners and researchers.

BACKGROUND: Making landfall in Rockport, Texas in August 2017, Hurricane Harvey resulted in unprecedented flooding, displacing tens of thousands of people, and creating environmental hazards and exposures for many more. OBJECTIVE: We describe a collaborative project to establish the Texas Flood Registry to track the health and housing impacts of major flooding events. METHODS: Those who enroll in the registry answer retrospective questions regarding the impact of storms on their health and housing status. We recruit both those who did and did not flood during storm events to enable key comparisons. We leverage partnerships with multiple local health departments, community groups, and media outlets to recruit broadly. We performed a preliminary analysis using multivariable logistic regression and a binomial Bayesian conditional autoregressive (CAR) spatial model. RESULTS: We find that those whose homes flooded, or who came into direct skin contact with flood water, are more likely to experience a series of self-reported health effects. Median household income is inversely related to adverse health effects, and spatial analysis provides important insights within the modeling approach. SIGNIFICANCE: Global climate change is likely to increase the number and intensity of rainfall events, resulting in additional health burdens. Population-level data on the health and housing impacts of major flooding events is imperative in preparing for our planet's future.