Public Health Surveillance Tool Research Articles

Wastewater-based proteomics: A proof-of-concept for advancing early warning system for infectious diseases and immune response monitoring

In this proof-of-concept study, a new mass spectrometry-based framework was introduced for concurrent tracking of infectious disease prevalence and community responses. The study focused on the detection of SARS-CoV-2 as the test pathogen and C-reactive protein (CRP) as the representative acute phase response protein. Through mass spectrometry (MS), the research provided preliminary insights into the prevalence of the virus and community acute immune responses, suggesting its strong potential as an early warning system. The high specificity and sensitivity of MS, combined with wastewater-based epidemiology's ability to provide a population-level perspective on virus prevalence, make it a valuable tool for public health surveillance. The study's findings demonstrate the utility of targeted proteomics technology in detecting specific protein biomarkers associated with SARS-CoV-2 infection and inflammation in complex wastewater samples. This approach has advantages over traditional RNA-based methods, including the ability to simultaneously detect acute-phase response proteins such as CRP. The study lays the foundation for future research towards refining analytical techniques to extract more precise data from complex matrices. SynopsisWBE proteomics holds a strong potential in wastewater surveillance for pathogens and disease outcomes

Simultaneous detection and quantification of multiple pathogen targets in wastewater

Wastewater-based epidemiology has emerged as a critical tool for public health surveillance, building on decades of environmental surveillance work for pathogens such as poliovirus. Work to date has been limited to monitoring a single pathogen or small numbers of pathogens in targeted studies; however, few studies consider simultaneous quantitative analysis of a wide variety of pathogens, which could greatly increase the utility of wastewater surveillance. We developed a novel quantitative multi-pathogen surveillance approach (35 pathogen targets including bacteria, viruses, protozoa, and helminths) using TaqMan Array Cards (TAC) and applied the method on concentrated wastewater samples collected at four wastewater treatment plants in Atlanta, GA from February to October of 2020. From sewersheds serving approximately 2 million people, we detected a wide range of targets including many we expected to find in wastewater (e.g., enterotoxigenic E. coli and Giardia in 97% of 29 samples at stable concentrations) as well as unexpected targets including Strongyloides stercoralis (a human threadworm rarely observed in the USA). Other notable detections included SARS-CoV-2, but also several pathogen targets that are not commonly included in wastewater surveillance like Acanthamoeba spp., Balantidium coli, Entamoeba histolytica, astrovirus, norovirus, and sapovirus. Our data suggest broad utility in expanding the scope of enteric pathogen surveillance in wastewaters, with potential for application in a variety of settings where pathogen quantification in fecal waste streams can inform public health surveillance and selection of control measures to limit infections.

Analysis of perceptions and emotional repercussions in Twitter users in Colombia during the COVID-19 pandemic

IntroductionThe COVID-19 pandemic has negatively impacted mental health. Up to a quarter of the population has reported mental health disorders. This has been studied mainly from a nosological perspective, according to diagnostic criteria. Nevertheless, we did not find studies that have explored the daily expressions of the population. Our objective was to evaluate the perceptions of the COVID-19 pandemic and its repercussions on the emotional well-being of the Colombian population. MethodsWe performed a Twitter metrics and trend analysis. Initially, in the trend analysis, we calculated the average duration in hours of the 20 most popular trending topics of the day in Colombia and we grouped them into trends related to COVID-19 and unrelated trends. Subsequently, we identified dates of events associated with the pandemic relevant to the country, and they were related to the behaviour of the trends studied. Additionally, we did an exploratory analysis of these, selected the tweets with the greatest reach and categorised them in an inductive way to analyse them qualitatively. ResultsIssues not related to COVID-19 were more far-reaching than those related to coronavirus. However, a rise in these issues was seen on some dates consistent with important events in Colombia. We found expressions of approval and disapproval, solidarity and accusation. Inductively, we identified categories of informative tweets, humour, fear, stigma and discrimination, politics and entities, citizen complaints, and self-care and optimism. ConclusionsThe impact of the COVID-19 pandemic generates different reactions in the population, which increasingly have more tools to express themselves and know the opinions of others. Social networks play a fundamental role in the communication of the population, so this content could serve as a public health surveillance tool and a useful and accessible means of communication in the management of health crises.

Proof-of-Concept: Smartphone- and Cloud-Based Artificial Intelligence Quantitative Analysis System (SCAISY) for SARS-CoV-2-Specific IgG Antibody Lateral Flow Assays.

Smartphone-based point-of-care testing (POCT) is rapidly emerging as an alternative to traditional screening and laboratory testing, particularly in resource-limited settings. In this proof-of-concept study, we present a smartphone- and cloud-based artificial intelligence quantitative analysis system (SCAISY) for relative quantification of SARS-CoV-2-specific IgG antibody lateral flow assays that enables rapid evaluation (<60 s) of test strips. By capturing an image with a smartphone camera, SCAISY quantitatively analyzes antibody levels and provides results to the user. We analyzed changes in antibody levels over time in more than 248 individuals, including vaccine type, number of doses, and infection status, with a standard deviation of less than 10%. We also tracked antibody levels in six participants before and after SARS-CoV-2 infection. Finally, we examined the effects of lighting conditions, camera angle, and smartphone type to ensure consistency and reproducibility. We found that images acquired between 45° and 90° provided accurate results with a small standard deviation and that all illumination conditions provided essentially identical results within the standard deviation. A statistically significant correlation was observed (Spearman correlation coefficient: 0.59, p = 0.008; Pearson correlation coefficient: 0.56, p = 0.012) between the OD450 values of the enzyme-linked immunosorbent assay and the antibody levels obtained by SCAISY. This study suggests that SCAISY is a simple and powerful tool for real-time public health surveillance, enabling the acceleration of quantifying SARS-CoV-2-specific antibodies generated by either vaccination or infection and tracking of personal immunity levels.

The DataHarmonizer: a tool for faster data harmonization, validation, aggregation and analysis of pathogen genomics contextual information.

Pathogen genomics is a critical tool for public health surveillance, infection control, outbreak investigations as well as research. In order to make use of pathogen genomics data, they must be interpreted using contextual data (metadata). Contextual data include sample metadata, laboratory methods, patient demographics, clinical outcomes and epidemiological information. However, the variability in how contextual information is captured by different authorities and how it is encoded in different databases poses challenges for data interpretation, integration and their use/re-use. The DataHarmonizer is a template-driven spreadsheet application for harmonizing, validating and transforming genomics contextual data into submission-ready formats for public or private repositories. The tool's web browser-based JavaScript environment enables validation and its offline functionality and local installation increases data security. The DataHarmonizer was developed to address the data sharing needs that arose during the COVID-19 pandemic, and was used by members of the Canadian COVID Genomics Network (CanCOGeN) to harmonize SARS-CoV-2 contextual data for national surveillance and for public repository submission. In order to support coordination of international surveillance efforts, we have partnered with the Public Health Alliance for Genomic Epidemiology to also provide a template conforming to its SARS-CoV-2 contextual data specification for use worldwide. Templates are also being developed for One Health and foodborne pathogens. Overall, the DataHarmonizer tool improves the effectiveness and fidelity of contextual data capture as well as its subsequent usability. Harmonization of contextual information across authorities, platforms and systems globally improves interoperability and reusability of data for concerted public health and research initiatives to fight the current pandemic and future public health emergencies. While initially developed for the COVID-19 pandemic, its expansion to other data management applications and pathogens is already underway.

A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic

The potential utility of wastewater-based epidemiology as an early warning tool has been explored widely across the globe during the current COVID-19 pandemic. Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive work has been conducted to evaluate the relationship between viral concentration and COVID-19 case numbers at the catchment areas of sewage treatment works (STWs) over time. However, no attempt has been made to develop a model that predicts wastewater concentration at fine spatio-temporal resolutions covering an entire country, a necessary step towards using wastewater monitoring for the early detection of local outbreaks.We consider weekly averages of flow-normalised viral concentration, reported as the number of SARS-CoV-2N1 gene copies per litre (gc/L) of wastewater available at 303 STWs over the period between 1 June 2021 and 30 March 2022. We specify a spatially continuous statistical model that quantifies the relationship between weekly viral concentration and a collection of covariates covering socio-demographics, land cover and virus associated genomic characteristics at STW catchment areas while accounting for spatial and temporal correlation.We evaluate the model’s predictive performance at the catchment level through 10-fold cross-validation. We predict the weekly viral concentration at the population-weighted centroid of the 32,844 lower super output areas (LSOAs) in England, then aggregate these LSOA predictions to the Lower Tier Local Authority level (LTLA), a geography that is more relevant to public health policy-making. We also use the model outputs to quantify the probability of local changes of direction (increases or decreases) in viral concentration over short periods (e.g. two consecutive weeks).The proposed statistical framework can predict SARS-CoV-2 viral concentration in wastewater at high spatio-temporal resolution across England. Additionally, the probabilistic quantification of local changes can be used as an early warning tool for public health surveillance.

A methodology for the public health surveillance and epidemiologic analysis of outdoor falls that require an emergency medical services response

BackgroundFalls are a common cause of injury with significantly associated medical costs yet public health surveillance of injuries from falls is underdeveloped. In addition, the epidemiologic understanding of outdoor falls, which have been reported to account for 47% of all injurious falls, is scant. Here we present methods to use emergency medical services (EMS) data as a public health surveillance tool for fall injuries, including those that occur secondary to syncope and heat illness, with a focus on the scope and epidemiology of outdoor fall injuries.MethodsUsing the 2019 National Emergency Medical Services Information System (NEMSIS) data, we developed an approach to identify EMS encounters for fall injuries, syncope and heat illness. NEMSIS variables used in our algorithm included the EMS respondent’s impression of the encounter, the reported major symptoms and the cause of injury. With these data we identified injuries from falls and, using the NEMSIS data on the location of the encounter, we identified fall injuries as occurring indoors or outdoors. We present the descriptive epidemiology of the identified patients.ResultsThere were 1,854,909 injuries from falls that required an EMS response identified in the NEMSIS data, with 4% of those injuries secondary to episodes of syncope (n = 73,126) and heat illness. Sufficient data were available from 94% of injurious falls that they could be assigned to indoor or outdoor locations, with 9% of these fall injuries occurring outdoors. Among fall injuries identified as occurring outdoors, 85% occurred on streets and sidewalks. Patient age was the primary sociodemographic characteristic that varied by location of the injurious fall. Sixty-six percent of fall injuries that occurred indoors were among those age 65 years or older, while this figure was 34% for fall injuries occurring outdoors on a street or sidewalk.ConclusionThe occurrence of outdoor fall injuries identified in the NEMSIS data were substantially lower than reported in other data sets. However, numerically fall injuries occurring outdoors represent a substantial public health burden. The strengths and weaknesses of using this approach for routine public health surveillance of injuries from falls, syncope and heat illness are discussed.

Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Algorithm Development and Validation

Real-time air pollution monitoring is a valuable tool for public health and environmental surveillance. In recent years, there has been a dramatic increase in air pollution forecasting and monitoring research using artificial neural networks. Most prior work relied on modeling pollutant concentrations collected from ground-based monitors and meteorological data for long-term forecasting of outdoor ozone (O3), oxides of nitrogen, and fine particulate matter (PM2.5). Given that traditional, highly sophisticated air quality monitors are expensive and not universally available, these models cannot adequately serve those not living near pollutant monitoring sites. Furthermore, because prior models were built based on physical measurement data collected from sensors, they may not be suitable for predicting the public health effects of pollution exposure. This study aimed to develop and validate models to nowcast the observed pollution levels using web search data, which are publicly available in near real time from major search engines. We developed novel machine learning-based models using both traditional supervised classification methods and state-of-the-art deep learning methods to detect elevated air pollution levels at the US city level by using generally available meteorological data and aggregate web-based search volume data derived from Google Trends. We validated the performance of these methods by predicting 3 critical air pollutants (O3, nitrogen dioxide, and PM2.5) across 10 major US metropolitan statistical areas in 2017 and 2018. We also explore different variations of the long short-term memory model and propose a novel search term dictionary learner-long short-term memory model to learn sequential patterns across multiple search terms for prediction. The top-performing model was a deep neural sequence model long short-term memory, using meteorological and web search data, and reached an accuracy of 0.82 (F1-score 0.51) for O3, 0.74 (F1-score 0.41) for nitrogen dioxide, and 0.85 (F1-score 0.27) for PM2.5, when used for detecting elevated pollution levels. Compared with using only meteorological data, the proposed method achieved superior accuracy by incorporating web search data. The results show that incorporating web search data with meteorological data improves the nowcasting performance for all 3 pollutants and suggest promising novel applications for tracking global physical phenomena using web search data.

Wastewater Surveillance for Public Health: Beyond the Pandemic

Wastewater-based epidemiology (WBE) is a public health tool that enables informed, targeted responses to crises such as disease outbreaks and epidemics. In this paper, we discuss the significance of the practice of open science in generating social impact through policy decisions on day-to-day pandemic management when public institutions adopt WBE as part of the COVID-19 pandemic response. Further, we examine how open science helped communicate the importance and credibility of cutting-edge science to the public through continuous engagement using publicly available data, scientific communication interfaced through mass media, and information sharing via a live dashboard. Critical to the translation of science to policy was the philosophy of complete access to the entire knowledge generation process, results, and findings. We analyze the success of such an initiative in the city of Bengaluru in India amidst other academic studies in the country with regard to their translation to policy engagement. We posit that Bengaluru’s success may be due to the presence of an open science ethos, and multi-stakeholder inputs for knowledge creation and dissemination, leading to nuanced science communication which is empowering rather than patronizing. We further suggest policy-level recommendations for the incorporation of WBE as a public health surveillance tool and impress upon the interweaving of open science as part of scientific practice and its broader uptake by the policy ecosystem.

Nationwide public perceptions regarding the acceptance of using wastewater for community health monitoring in the United States.

To assess the levels of infection across communities during the coronavirus disease 2019 pandemic, researchers have measured severe acute respiratory syndrome coronavirus 2 RNA in feces dissolved in sewer water. This activity is colloquially known as sewer monitoring and is referred to as wastewater-based epidemiology in academic settings. Although global ethical principles have been described, sewer monitoring is unregulated for health privacy protection when used for public health surveillance in the United States. This study used Qualtrics XM, a national research panel provider, to recruit participants to answer an online survey. Respondents (N = 3,083) answered questions about their knowledge, perceptions of what is to be monitored, where monitoring should occur, and privacy concerns related to sewer monitoring as a public health surveillance tool. Furthermore, a privacy attitude questionnaire was used to assess the general privacy boundaries of respondents. Participants were more likely to support monitoring for diseases (92%), environmental toxins (92%), and terrorist threats (88%; e.g., anthrax). Two-third of the respondents endorsed no prohibition on location sampling scale (e.g., monitoring single residence to entire community was acceptable); the most common location category respondents wanted to prohibit sampling was at personal residences. Sewer monitoring is an emerging technology, and our study sheds light on perceptions that could benefit from educational programs in areas where public acceptance is comparatively lower. Respondents clearly communicated guard rails for sewer monitoring, and public opinion should inform future policy, application, and regulation measures.

Quantifying the Relationship between SARS-CoV-2 Wastewater Concentrations and Building-Level COVID-19 Prevalence at an Isolation Residence: A Passive Sampling Approach.

SARS-CoV-2 RNA loads can be detected in the excreta of individuals with COVID-19 and have demonstrated positive correlations with clinical infection trends. Consequently, wastewater-based epidemiology (WBE) approaches have been implemented globally as a public health surveillance tool to monitor community-level prevalence of infections. The majority of wastewater specimens are gathered as either composite samples via automatic samplers (autosamplers) or grab samples. However, autosamplers are expensive and can be challenging to maintain in cold weather, while grab samples are particularly susceptible to temporal variation when sampling sewage directly from complex matrices outside residential buildings. Passive sampling can provide an affordable, practical, and scalable sampling system while maintaining a reproducible SARS-CoV-2 signal. In this regard, we deployed tampons as passive samplers outside of a COVID-19 isolation unit (a segregated residence hall) at a university campus from 1 February 2021–21 May 2021. Samples (n = 64) were collected 3–5 times weekly and remained within the sewer for a median duration of 24 h. SARS-CoV-2 RNA was quantified using reverse-transcription quantitative polymerase chain reaction (RT-qPCR) targeting the N1 and N2 gene fragments. We quantified the mean viral load captured per individual and the association between the daily viral load and total persons, adjusting for covariates using multivariable models to provide a baseline estimate of viral shedding. Samples were processed through two distinct laboratory pipelines on campus, yielding highly correlated N2 concentrations. Data obtained here highlight the success of passive sampling utilizing tampons to capture SARS-CoV-2 in wastewater coming from a COVID-19 isolation residence, indicating that this method can help inform building-level public health responses.

Impact of COVID-19 Social Distancing Mandates on Gastrointestinal Pathogen Positivity: Secondary Data Analysis.

BackgroundAcute gastrointestinal (GI) illnesses are of the most common problems evaluated by physicians and some of the most preventable. There is evidence of GI pathogen transmission when people are in close contact. The COVID-19 pandemic led to the sudden implementation of widespread social distancing measures in the United States. There is strong evidence that social distancing measures impact the spread of SARS-CoV-2, and a growing body of research indicates that these measures also decrease the transmission of other respiratory pathogens.ObjectiveThis study aims to investigate the impact of COVID-19 social distancing mandates on the GI pathogen positivity rates.MethodsDeidentified GI Panel polymerase chain reaction test results from a routinely collected diagnostic database from January 1, 2019, through August 31, 2020, were analyzed for the GI pathogen positivity percentage. An interrupted time series analysis was performed, using social distancing mandate issue dates as the intervention date. The following 3 target organisms were chosen for the final analysis to represent different primary transmission routes: adenovirus F40 and 41, norovirus GI and GII, and Escherichia coli O157.ResultsIn total, 84,223 test results from 9 states were included in the final data set. With the exception of E coli O157 in Kansas, Michigan, and Nebraska, we observed an immediate decrease in positivity percentage during the week of social distancing mandates for all other targets and states. Norovirus GI and GII showed the most notable drop in positivity, whereas E coli O157 appeared to be least impacted by social distancing mandates. Although we acknowledge the analysis has a multiple testing problem, the majority of our significant results showed significance even below the .01 level.ConclusionsThis study aimed to investigate the impact of social distancing mandates for COVID-19 on GI pathogen positivity, and we discovered that social distancing measures in fact decreased GI pathogen positivity initially. The use of similar measures may prove useful in GI pathogen outbreaks. The use of a unique diagnostic database in this study exhibits the potential for its use as a public health surveillance tool.

The ethical dilemma of mobile phone data monitoring during COVID-19: The case for South Korea and the United States.

Governments across the world have integrated a variety of advanced technologies to respond to the COVID-19 crisis. In particular, the use of surveillance programs that leverage data and tools from mobile phones have become important components of public health strategies to contain the spread of SARS-CoV-2 across the globe. Currently, big technology companies around the world are helping governments evaluate the effectiveness of their social distancing protocols by examining and analyzing movements of millions of mobile phone users in order to determine how the virus is spreading across the various geographic locations, and the effectiveness of the various social distancing methods that have been implemented. The collection and use of individual mobile phone data as a public health surveillance tool presents tensions between several ethical priorities. Such a dilemma resides in the tensions between public health ethics goals and clinical ethics goals. While public health ethics pursues goals that seek to ensure the good of the community, such goals are often achieved at the expense of clinical ethics goals which emphasize individual autonomy and civil liberty. In using persons’ mobile phone data as a tool to fight the COVID-19 pandemic, we must address the tensions associated with weighing the needs of “the many” with ensuring the rights of the individual.

Insomnia as a concerned mental health issue during COVID-19 pandemic: A google trend analysis

IntroductionInsomnia is one of the most common major health issues during the COVID-19 pandemic. There has been limited evidence that showed the correlation between insomnia and COVID-19 using Google trend.ObjectivesTo investigate the impact of the COVID-19 pandemic on interest in insomnia, including national mental health by a Google trend analysis that implicitly represents the state of distress and concern for this pandemic.MethodsWe examined the Google trend search query data from these sleep-related keywords: insomnia, restless leg, and obstructive sleep apnea (OSA) from 1 Jan 2020 to 30 May 2020 and explored the correlation between the internet search volumes for insomnia and the cumulative number of new COVID-19 cases. In addition, we investigated the internet search pattern over time, before and during the COVID-19 pandemic.ResultsDuring the early phase of the COVID-19 pandemic between January and May 2020, the Relative Search Volumes (RSV) curves showed that the cumulative number of new COVID-19 cases was significantly correlated with the rising search for these keywords linking to sleep-related conditions as follows: ‘insomnia’ (r = 0.41, p &lt; 0.001), and ‘restless leg’ (r = 0.19, p = 0.009). However, it was not correlated with the keyword ‘OSA’ (r = -0.14, p = 0.07).ConclusionsThese findings emphasize the impact of the COVID-19 pandemic on insomnia and the crucial need for public mental health interventions to be offered and accessible. The Google trend could be used as a new tool for public mental health surveillance in a new normal lifestyle.DisclosureNo significant relationships.

Emergency department utilization for substance use disorders and mental health conditions during COVID-19.

As the emergency department (ED) has evolved into the de-facto site of care for a variety of substance use disorder (SUD) presentations, trends in ED utilization are an essential public health surveillance tool. Changes in ED visit patterns during the COVID-19 pandemic may reflect changes in access to outpatient treatment, changes in SUD incidence, or the unintended effects of public policy to mitigate COVID-19. We use a national emergency medicine registry to describe and characterize trends in ED visitation for SUDs since 2019. We included all ED visits identified in a national emergency medicine clinical quality registry, which included 174 sites across 33 states with data from January 2019 through June 2021. We defined SUD using ED visit diagnosis codes including: opioid overdose and opioid use disorder (OUD), alcohol use disorders (AUD), and other SUD. To characterize changes in ED utilization, we plotted the 3-week moving average ratio of visit counts in 2020 and 2021 as compared to visit counts in 2019. While overall ED visits declined in the early pandemic period and had not returned to 2019 baseline by June 2021, ED visit counts for SUD demonstrated smaller declines in March and April of 2020, so that the proportion of overall ED visits that were for SUD increased. Furthermore, in the second half of 2020, ED visits for SUD returned to baseline, and increased above baseline for OUD ever since May 2020. We observe distinct patterns in ED visitation for SUDs over the course of the COVID-19 pandemic, particularly for OUD for which ED visitation barely declined and now exceeds previous baselines. These trends likely demonstrate the essential role of hospital-based EDs in providing 24/7/365 care for people with SUDs and mental health conditions. Allocation of resources must be directed towards the ED as a de-facto safety net for populations in crisis.

Exploratory Data Analysis of Genomic Sequence of Variants of SARS-CoV-2 Reveals Sequence Divergence and Mutational Localization.

Whole genome sequencing has rapidly progressed in recent years, with sequencing the SARS-CoV-2 genomes, making it a more reliable clinical tool for public health surveillance. This development has resulted in the production of a large amount of genomic data used for various types of genomic exploration. However, without a proper standard protocol, the usage of genomic data for analyzing various biological phenomena, such as mutation and evolution, may result in a propagating risk of using an unvalidated data set. This process could lead to irregular data being generated along with a high risk of altered analysis. Thus, the current study lays out the foundation for a preprocess pipeline using data analysis to analyze the genomic data set for its accuracy. We have used the recent example of SARS-CoV-2 to demonstrate the process overflow that can be utilized for various kinds of biological exploration such as understanding mutational events, evolutionary divergence, and speciation. Our analysis reveals a significant amount of sequence divergence in the gamma variant as compared with the reference genome thereby making the variant less infective and deadly. Moreover, we found regions in the genomic sequence that is more prone to mutational localization thereby altering the structural integrity of the virus resulting in a more reliable molecular viral mechanism. We believe that the current work will help for an initial check of the genomic data followed by the biological assessment of the process overflow which will be beneficial for the variant analysis and mutational uprising.

Psychometric Validation of the Wisconsin Community Participation Scale in a Sample of People with Chronic Health Conditions and Disabilities Living in the Community

Increasing community participation can reduce the risk for functional disabilities; participation is influenced by person and environment contextual factors. Development and validation of a brief community participation assessment can advance and support evidence-based assessment in clinical rehabilitation counseling practice. It will be an invaluable rehabilitation and public health surveillance tool that can be used to gauge the health conditions and participation of people with disabilities. The current study evaluated and validated the Wisconsin Community Participation Scale (WCPS) in 982 individuals with chronic health conditions and disabilities. Participants indicated five most meaningful life roles: (a) being able to get around with or without help, (b) live independently with or without help, (c) live a healthy lifestyle, (d) work, and (e) engage in leisure and recreation activities. The WCPS scores were positively associated with physical health, mental health, and life satisfaction and negatively related to functional disability in the theoretically expected directions. The WCPS can help rehabilitation counselors assess their clients’ current level of participation in meaningful life roles that are important to them. Rehabilitation counselors can use the WCPS assessment data to guide treatment planning and empower clients to build confidence and self-efficacy to participate in personally meaningful activities in the community.

APCDs can Provide Important Insights for Surveilling the Opioid Epidemic, With Caveats.

State-level all-payer claims databases (APCDs) are a possible new public health surveillance tool, but their reliability is unclear. We compared Colorado's APCD with other state-level databases for use in monitoring the opioid epidemic (Colorado Hospital Association and Colorado's Prescription Drug Monitoring Program database for 2010-2017), using descriptive analyses comparing quarterly counts/rates of opioid-involved inpatient and emergency department visits and counts/rates of 30-day opioid fills between databases. Utilization is lower in the Colorado APCD than the other databases for all outcomes but trends are parallel and consistent between databases. State APCDs hold promise for researchers, but they may be better suited to individual-level analyses or comparisons of providers than for surveillance of public health trends related to addiction.

Possibility of Detection of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) through Wastewater in Developing Countries

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was detected in Wuhan, China. The clinical manifestation of COVID-19 varies from asymptomatic to severe infection. The World Health Organization has reported over 248 million cases and more than 5 million deaths worldwide due to COVID-19. Additionally, developing countries are still struggling to achieve the rapid diagnosis of suspected cases to break the chain of transmission of COVID-19. There may be undiagnosed or missing cases in these countries. Various studies have reported that the SARS-CoV-2 can be discharged in the stool and other bodily fluids. Moreover, it is possible that SARS-CoV-2 can be transmitted in the environment via municipal wastewater. In developing countries, such as Nepal, the molecular-based diagnosis of SARS-CoV-2 for the entire population is not practical owing to insufficient diagnostic material and budgetary constraints. Wastewater-based epidemiology (WBE) is a promising public health surveillance tool for the early warning of disease outbreaks and the understanding the prevalence of viruses for the development of intervention measures. This study highlights the importance of the utilization of WBE as an alternative tool for the mass detection of SARS-CoV-2 RNA in wastewater.

Validating and optimizing the method for molecular detection and quantification of SARS-CoV-2 in wastewater

Wastewater surveillance of SARS-CoV-2 has become a promising tool to estimate population-level changes in community infections and the prevalence of COVID-19 disease. Although many studies have reported the detection and quantification of SARS-CoV-2 in wastewater, remarkable variation remains in the methodology. In this study, we validated a molecular testing method by concentrating viruses from wastewater using ultrafiltration and detecting SARS-CoV-2 using one-step RT-qPCR assay. The following parameters were optimized including sample storage condition, wastewater pH, RNA extraction and RT-qPCR assay by quantification of SARS-CoV-2 or spiked human coronavirus strain 229E (hCoV-229E). Wastewater samples stored at 4 °C after collection showed significantly enhanced detection of SARS-CoV-2 with approximately 2–3 PCR-cycle threshold (Ct) values less when compared to samples stored at −20 °C. Pre-adjustment of the wastewater pH to 9.6 to aid virus desorption followed by pH readjustment to neutral after solid removal significantly increased the recovery of spiked hCoV-229E. Of the five commercially available RNA isolation kits evaluated, the MagMAX-96 viral RNA isolation kit showed the best recovery of hCoV-229E (50.1 ± 20.1%). Compared with two-step RT-qPCR, one-step RT-qPCR improved sensitivity for SARS-CoV-2 detection. Salmon DNA was included for monitoring PCR inhibition and pepper mild mottle virus (PMMoV), a fecal indicator indigenous to wastewater, was used to normalize SARS-CoV-2 levels in wastewater. Our method for molecular detection of SARS-CoV-2 in wastewater provides a useful tool for public health surveillance of COVID-19.