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.

Denoising Non-Stationary Signals via Dynamic Multivariate Complex Wavelet Thresholding.

Over the past few years, we have seen an increased need to analyze the dynamically changing behaviors of economic and financial time series. These needs have led to significant demand for methods that denoise non-stationary time series across time and for specific investment horizons (scales) and localized windows (blocks) of time. Wavelets have long been known to decompose non-stationary time series into their different components or scale pieces. Recent methods satisfying this demand first decompose the non-stationary time series using wavelet techniques and then apply a thresholding method to separate and capture the signal and noise components of the series. Traditionally, wavelet thresholding methods rely on the discrete wavelet transform (DWT), which is a static thresholding technique that may not capture the time series of the estimated variance in the additive noise process. We introduce a novel continuous wavelet transform (CWT) dynamically optimized multivariate thresholding method (WaveL2E). Applying this method, we are simultaneously able to separate and capture the signal and noise components while estimating the dynamic noise variance. Our method shows improved results when compared to well-known methods, especially for high-frequency signal-rich time series, typically observed in finance.

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.

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).

Economic and mental health impacts of multiple adverse events: Hurricane Harvey, other flooding events, and the COVID-19 pandemic.

OBJECTIVES: To assess the economic and mental health impacts of COVID-19 in the presence of previous exposure to flooding events. METHODS: Starting in April 2018, the Texas Flood Registry (TFR) invited residents to complete an online survey regarding their experiences with Hurricane Harvey and subsequent flooding events. Starting in April 2020, participants nationwide were invited to complete a brief online survey on their experiences during the pandemic. This study includes participants in the TFR (N = 20,754) and the COVID-19 Registry (N = 8568) through October 2020 (joint N = 2929). Logistic regression and generalized estimating equations were used to examine the relationship between exposure to flooding events and the economic and mental health impacts of COVID-19. RESULTS: Among COVID-19 registrants, 21% experienced moderate to severe anxiety during the pandemic, and 7% and 12% of households had difficulty paying rent and bills, respectively. Approximately 17% of Black and 15% of Hispanic households had difficulty paying rent, compared to 5% of non-Hispanic white households. The odds of COVID-19 income loss are 1.20 (1.02, 1.40) times higher for those who previously had storm-related home damage compared to those who did not and 3.84 (3.25-4.55) times higher for those who experienced Harvey income loss compared to those who did not. For registrants for whom Harvey was a severe impact event, the odds of having more severe anxiety during the pandemic are 5.14 (4.02, 6.58) times higher than among registrants for whom Harvey was a no meaningful impact event. CONCLUSIONS: Multiple crises can jointly and cumulatively shape health and wellbeing outcomes. This knowledge can help craft emergency preparation and intervention programs.

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.

Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods.

Humans are exposed to a diverse mixture of chemical and non-chemical exposures across their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure science and related technologies enable the investigation of the health impacts of mixtures. While existing statistical methods can address the most basic questions related to the association between environmental mixtures and health endpoints, there were gaps in our ability to learn from mixtures data in several common epidemiologic scenarios, including high correlation among health and exposure measures in space and/or time, the presence of missing observations, the violation of important modeling assumptions, and the presence of computational challenges incurred by current implementations. To address these and other challenges, NIEHS initiated the Powering Research through Innovative methods for Mixtures in Epidemiology (PRIME) program, to support work on the development and expansion of statistical methods for mixtures. Six independent projects supported by PRIME have been highly productive but their methods have not yet been described collectively in a way that would inform application. We review 37 new methods from PRIME projects and summarize the work across previously published research questions, to inform methods selection and increase awareness of these new methods. We highlight important statistical advancements considering data science strategies, exposure-response estimation, timing of exposures, epidemiological methods, the incorporation of toxicity/chemical information, spatiotemporal data, risk assessment, and model performance, efficiency, and interpretation. Importantly, we link to software to encourage application and testing on other datasets. This review can enable more informed analyses of environmental mixtures. We stress training for early career scientists as well as innovation in statistical methodology as an ongoing need. Ultimately, we direct efforts to the common goal of reducing harmful exposures to improve public health.

Bayesian variable selection for understanding mixtures in environmental exposures.

Social and environmental stressors are crucial factors in child development. However, there exists a multitude of measurable social and environmental factors-the effects of which may be cumulative, interactive, or null. Using a comprehensive cohort of children in North Carolina, we study the impact of social and environmental variables on 4th end-of-grade exam scores in reading and mathematics. To identify the essential factors that predict these educational outcomes, we design new tools for Bayesian linear variable selection using decision analysis. We extract a predictive optimal subset of explanatory variables by coupling a loss function with a novel model-based penalization scheme, which leads to coherent Bayesian decision analysis and empirically improves variable selection, estimation, and prediction on simulated data. The Bayesian linear model propagates uncertainty quantification to all predictive evaluations, which is important for interpretable and robust model comparisons. These predictive comparisons are conducted out-of-sample with a customized approximation algorithm that avoids computationally intensive model refitting. We apply our variable selection techniques to identify the joint collection of social and environmental stressors-and their interactions-that offer clear and quantifiable improvements in prediction of reading and mathematics exam scores.

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.

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.

A spatiotemporal case-crossover model of asthma exacerbation in the City of Houston.

Case-crossover design is a popular construction for analyzing the impact of a transient effect, such as ambient pollution levels, on an acute outcome, such as an asthma exacerbation. Case-crossover design avoids the need to model individual, time-varying risk factors for cases by using cases as their own 'controls', chosen to be time periods for which individual risk factors can be assumed constant and need not be modelled. Many studies have examined the complex effects of the control period structure on model performance, but these discussions were simplified when case-crossover design was shown to be equivalent to various specifications of Poisson regression when exposure is considered constant across study participants. While reasonable for some applications, there are cases where such an assumption does not apply due to spatial variability in exposure, which may affect parameter estimation. This work presents a spatiotemporal model, which has temporal case-crossover and a geometrically aware spatial random effect based on the Hausdorff distance. The model construction incorporates a residual spatial structure in cases when the constant assumption exposure is not reasonable and when spatial regions are irregular.

City-Specific Air Quality Warnings for Improved Asthma Self-Management.

INTRODUCTION: This study presents a framework for identifying "high-risk" days for asthma attacks associated with elevated concentrations of criteria pollutants using local information to warn citizens on days when the concentrations differ from Environmental Protection Agency Air Quality Index (AQI) warnings. Studies that consider the unique mixture of pollutants and the health data specific to a city provide additional information for asthma self-management. This framework is applied to air pollution and asthma data to identify supplemental warning days in Houston, Texas. METHODS: A four-step framework was established to identify days with pollutant levels that pose meaningful increased risk for asthma attacks compared with baseline. Historical associations between 18,542 ambulance-treated asthma attacks and air pollutant concentrations in Houston, Texas (2004-2016; analyzed in 2018), were analyzed using a case-crossover study design with conditional logistic regression. Days with historically high associations between pollution and asthma attacks were identified as supplemental warning days. RESULTS: Days with 8-hour maximum ozone >66.6 parts per billion for the 3 previous days and same-day 24-hour nitrogen dioxide >19.3 parts per billion pose an RR of 15% above baseline; concentrations above these levels pose an increased risk of 15% (RR=1.15, 95% CI=1.14, 1.16) and 30% (RR=1.30, 95% CI=1.29, 1.32), respectively. These warnings add an additional 12% days per year over the AQI warnings. CONCLUSIONS: Houston uses this framework to identify supplemental air quality warnings to improve asthma self-management. Supplemental days reflect risk lower than the National Ambient Air Quality Standards and consecutive poor air quality days, differing from the AQI.

Analyzing Who, When, and Where: Data for Better Targeting of Resources for School-Based Asthma Interventions.

BACKGROUND: Rates of uncontrolled asthma vary by demographics, space, and time. This article uses data on ambulance-treated asthma attacks in children to analyze these variations so that school districts can improve their asthma management interventions. METHODS: Incidence rates of 1826 ambulance-treated asthma attacks for children aged 5-18 years were calculated for school zones for elementary, middle, and high schools in the Houston (Texas) Independent School District (HISD). Zones with rates in the upper quartile were identified as the highest rate zones and were compared with other school zones in the district by demographics, location, and timing of attacks. RESULTS: The ambulance-treated asthma rate was respectively 5, 3, and 2 times greater in the highest rate school zones compared with all other school zones for those school levels. Ambulance-treated asthma attacks in the high-rate school zones occurred most at midday and in the evening and high-rate zones were often geographically contiguous. Schools in the high-rate zones had a higher percent of socioeconomically disadvantaged students and were more often without a school nurse. CONCLUSION: Spatial and temporal analysis of ambulance data can be valuable tools for schools to focus policy and program interventions for the students in need of improved asthma management.

Cross-Disciplinary Consultancy to Enhance Predictions of Asthma Exacerbation Risk in Boston.

This paper continues an initiative conducted by the International Society for Disease Surveillance with funding from the Defense Threat Reduction Agency to connect near-term analytical needs of public health practice with technical expertise from the global research community. The goal is to enhance investigation capabilities of day-to-day population health monitors. A prior paper described the formation of consultancies for requirements analysis and dialogue regarding costs and benefits of sustainable analytic tools. Each funded consultancy targets a use case of near-term concern to practitioners. The consultancy featured here focused on improving predictions of asthma exacerbation risk in demographic and geographic subdivisions of the city of Boston, Massachusetts, USA based on the combination of known risk factors for which evidence is routinely available. A cross-disciplinary group of 28 stakeholders attended the consultancy on March 30-31, 2016 at the Boston Public Health Commission. Known asthma exacerbation risk factors are upper respiratory virus transmission, particularly in school-age children, harsh or extreme weather conditions, and poor air quality. Meteorological subject matter experts described availability and usage of data sources representing these risk factors. Modelers presented multiple analytic approaches including mechanistic models, machine learning approaches, simulation techniques, and hybrids. Health department staff and local partners discussed surveillance operations, constraints, and operational system requirements. Attendees valued the direct exchange of information among public health practitioners, system designers, and modelers. Discussion finalized design of an 8-year de-identified dataset of Boston ED patient records for modeling partners who sign a standard data use agreement.

Using community level strategies to reduce asthma attacks triggered by outdoor air pollution: a case crossover analysis.

BACKGROUND: Evidence indicates that asthma attacks can be triggered by exposure to ambient air pollutants, however, detailed pollution information is missing from asthma action plans. Asthma is commonly associated with four criteria pollutants with standards derived by the United States Environmental Protection Agency. Since multiple pollutants trigger attacks and risks depend upon city-specific mixtures of pollutants, there is lack of specific guidance to reduce exposure. Until multi-pollutant statistical modeling fully addresses this gap, some guidance on pollutant attack risk is required. This study examines the risks from exposure to the asthma-related pollutants in a large metropolitan city and defines the city-specific association between attacks and pollutant mixtures. Our goal is that city-specific pollution risks be incorporated into individual asthma action plans as additional guidance to prevent attacks. METHODS: Case-crossover analysis and conditional logistic regression were used to measure the association between ozone, fine particulate matter, nitrogen dioxide, sulfur dioxide and carbon monoxide pollution and 11,754 emergency medical service ambulance treated asthma attacks in Houston, Texas from 2004-2011. Both single and multi-pollutant models are presented. RESULTS: In Houston, ozone and nitrogen dioxide are important triggers (RR = 1.05; 95% CI: 1.00, 1.09), (RR = 1.10; 95% CI: 1.05, 1.15) with 20 and 8 ppb increase in ozone and nitrogen dioxide, respectively, in a multi-pollutant model. Both pollutants are simultaneously high at certain times of the year. The risk attributed to these pollutants differs when they are considered together, especially as concentrations increase. Cumulative exposure for ozone (0-2 day lag) is of concern, whereas for nitrogen dioxide the concern is with single day exposure. Persons at highest risk are aged 46-66, African Americans, and males. CONCLUSIONS: Accounting for cumulative and concomitant outdoor pollutant exposure is important to effectively attribute risk for triggering of an asthma attack, especially as concentrations increase. Improved asthma action plans for Houston individuals should warn of these pollutants, their trends, correlation and cumulative effects. Our Houston based study identifies nitrogen dioxide levels and the three-day exposure to ozone to be of concern whereas current single pollutant based national standards do not.

Geospatial analysis for targeting out-of-hospital cardiac arrest intervention.

BACKGROUND: Bystander cardiopulmonary resuscitation (BCPR) provides an opportunity for decreasing cardiac mortality. Rates of out-of-hospital cardiac arrest (OHCA) in which resuscitation was performed vary within cities and across demographics. PURPOSE: To identify contiguous geographic census tracts with high OHCA, low BCPR rates and high-risk demographics to effectively target culturally appropriate community-based intervention planning. METHODS: In 2012, a cohort of 11,389 emergency medical services (EMS) OHCA cases from Houston TX (2004-2011) was linked to census tracts. Multivariable logistic regression analyses were used to identify demographics of contiguous geographic census tracts with the highest OHCA rates. Within these tracts, BCPR rates were evaluated. The combination of information was used to develop a plan to better target interventions. RESULTS: Contiguous census tracts of high OHCA rates were identified; the average rate per 100,000 within versus outside the identified tracts is 106.0 (SD 23.7) to 55.8 (SD 19.7). Tracts with a low BCPR rate (37.7%) relative to a high OHCA rate were identified. In a separate analysis, individuals at highest relative risk of OHCA were found to be African Americans, to have low income or education levels, and to be older individuals. For every 1% increase in African Americans in a census tract, there is an increase of 2.7% in the relative risk of the census tract belonging to a high-OHCA-rate region (95% CI=2.0%, 3.5%). CONCLUSIONS: Geospatial analysis can provide important information on the contiguous areas of high OHCA rates and low BCPR rates with the aim of more effectively targeting interventions and ultimately decreasing cardiac deaths.

A case-crossover analysis of out-of-hospital cardiac arrest and air pollution.

BACKGROUND: Evidence of an association between the exposure to air pollution and overall cardiovascular morbidity and mortality is increasingly found in the literature. However, results from studies of the association between acute air pollution exposure and risk of out-of-hospital cardiac arrest (OHCA) are inconsistent for fine particulate matter, and, although pathophysiological evidence indicates a plausible link between OHCA and ozone, none has been reported. Approximately 300 000 persons in the United States experience an OHCA each year, of which >90% die. Understanding the association provides important information to protect public health. METHODS AND RESULTS: The association between OHCA and air pollution concentrations hours and days before onset was assessed by using a time-stratified case-crossover design using 11 677 emergency medical service-logged OHCA events between 2004 and 2011 in Houston, Texas. Air pollution concentrations were obtained from an extensive area monitor network. An average increase of 6 µg/m(3) in fine particulate matter 2 days before onset was associated with an increased risk of OHCA (1.046; 95% confidence interval, 1.012-1.082). A 20-ppb ozone increase for the 8-hour average daily maximum was associated with an increased risk of OHCA on the day of the event (1.039; 95% confidence interval, 1.005-1.073). Each 20-ppb increase in ozone in the previous 1 to 3 hours was associated with an increased risk of OHCA (1.044; 95% confidence interval, 1.004-1.085). Relative risk estimates were higher for men, blacks, or those aged >65 years. CONCLUSIONS: The findings confirm the link between OHCA and fine particulate matter and introduce evidence of a similar link with ozone.

Estimating the Term Structure With a Semiparametric Bayesian Hierarchical Model: An Application to Corporate Bonds.

The term structure of interest rates is used to price defaultable bonds and credit derivatives, as well as to infer the quality of bonds for risk management purposes. We introduce a model that jointly estimates term structures by means of a Bayesian hierarchical model with a prior probability model based on Dirichlet process mixtures. The modeling methodology borrows strength across term structures for purposes of estimation. The main advantage of our framework is its ability to produce reliable estimators at the company level even when there are only a few bonds per company. After describing the proposed model, we discuss an empirical application in which the term structure of 197 individual companies is estimated. The sample of 197 consists of 143 companies with only one or two bonds. In-sample and out-of-sample tests are used to quantify the improvement in accuracy that results from approximating the term structure of corporate bonds with estimators by company rather than by credit rating, the latter being a popular choice in the financial literature. A complete description of a Markov chain Monte Carlo (MCMC) scheme for the proposed model is available as Supplementary Material.