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Nevin Manimala Statistics

Wastewater-Based Epidemiology for COVID-19: Handling qPCR Nondetects and Comparing Spatially Granular Wastewater and Clinical Data Trends

ACS ES T Water. 2022 Jul 29:acsestwater.2c00053. doi: 10.1021/acsestwater.2c00053. Online ahead of print.

ABSTRACT

Wastewater-based epidemiology (WBE) is a useful complement to clinical testing for managing COVID-19. While community-scale wastewater and clinical data frequently correlate, less is known about subcommunity relationships between the two data types. Moreover, nondetects in qPCR wastewater data are typically handled through methods known to bias results, overlooking perhaps better alternatives. We address these knowledge gaps using data collected from September 2020-June 2021 in Davis, California (USA). We hypothesize that coupling the expectation maximization (EM) algorithm with the Markov Chain Monte Carlo (MCMC) method could improve estimation of “missing” values in wastewater qPCR data. We test this hypothesis by applying EM-MCMC to city wastewater treatment plant data and comparing output to more conventional nondetect handling methods. Dissimilarities in results (i) underscore the importance of specifying nondetect handling method in reporting and (ii) suggest that using EM-MCMC may yield better agreement between community-scale clinical and wastewater data. We also present a novel framework for spatially aligning clinical data with wastewater data collected upstream of a treatment plant (i.e., distributed across a sewershed). Applying the framework to data from Davis reveals reasonable agreement between wastewater and clinical data at highly granular spatial scales-further underscoring the public-health value of WBE.

PMID:37552742 | PMC:PMC9397567 | DOI:10.1021/acsestwater.2c00053

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Nevin Manimala Statistics

Rapid Implementation of High-Frequency Wastewater Surveillance of SARS-CoV-2

ACS ES T Water. 2022 Jul 1:acsestwater.2c00094. doi: 10.1021/acsestwater.2c00094. Online ahead of print.

ABSTRACT

There have been over 507 million cases of COVID-19, the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulting in 6 million deaths globally. Wastewater surveillance has emerged as a valuable tool in understanding SARS-CoV-2 burden in communities. The National Wastewater Surveillance System (NWSS) partnered with the United States Geological Survey (USGS) to implement a high-frequency sampling program. This report describes basic surveillance and sampling statistics as well as a comparison of SARS-CoV-2 trends between high-frequency sampling 3-5 times per week, referred to as USGS samples, and routine sampling 1-2 times per week, referred to as NWSS samples. USGS samples provided a more nuanced impression of the changes in wastewater trends, which could be important in emergency response situations. Despite the rapid implementation time frame, USGS samples had similar data quality and testing turnaround times as NWSS samples. Ensuring there is a reliable sample collection and testing plan before an emergency arises will aid in the rapid implementation of a high-frequency sampling approach. High-frequency sampling requires a constant flow of information and supplies throughout sample collection, testing, analysis, and data sharing. High-frequency sampling may be a useful approach for increased resolution of disease trends in emergency response.

PMID:37552727 | PMC:PMC9291391 | DOI:10.1021/acsestwater.2c00094

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Nevin Manimala Statistics

Subsewershed SARS-CoV-2 Wastewater Surveillance and COVID-19 Epidemiology Using Building-Specific Occupancy and Case Data

ACS ES T Water. 2022 May 13:acsestwater.2c00059. doi: 10.1021/acsestwater.2c00059. Online ahead of print.

ABSTRACT

To evaluate the use of wastewater-based surveillance and epidemiology to monitor and predict SARS-CoV-2 virus trends, over the 2020-2021 academic year we collected wastewater samples twice weekly from 17 manholes across Virginia Tech’s main campus. We used data from external door swipe card readers and student isolation/quarantine status to estimate building-specific occupancy and COVID-19 case counts at a daily resolution. After analyzing 673 wastewater samples using reverse transcription quantitative polymerase chain reaction (RT-qPCR), we reanalyzed 329 samples from isolation and nonisolation dormitories and the campus sewage outflow using reverse transcription digital droplet polymerase chain reaction (RT-ddPCR). Population-adjusted viral copy means from isolation dormitory wastewater were 48% and 66% higher than unadjusted viral copy means for N and E genes (1846/100 mL to 2733/100 mL/100 people and 2312/100 mL to 3828/100 mL/100 people, respectively; n = 46). Prespecified analyses with random-effects Poisson regression and dormitory/cluster-robust standard errors showed that the detection of N and E genes were associated with increases of 85% and 99% in the likelihood of COVID-19 cases 8 days later (incident-rate ratio (IRR) = 1.845, p = 0.013 and IRR = 1.994, p = 0.007, respectively; n = 215), and one-log increases in swipe card normalized viral copies (copies/100 mL/100 people) for N and E were associated with increases of 21% and 27% in the likelihood of observing COVID-19 cases 8 days following sample collection (IRR = 1.206, p < 0.001, n = 211 for N; IRR = 1.265, p < 0.001, n = 211 for E). One-log increases in swipe normalized copies were also associated with 40% and 43% increases in the likelihood of observing COVID-19 cases 5 days after sample collection (IRR = 1.403, p = 0.002, n = 212 for N; IRR = 1.426, p < 0.001, n = 212 for E). Our findings highlight the use of building-specific occupancy data and add to the evidence for the potential of wastewater-based epidemiology to predict COVID-19 trends at subsewershed scales.

PMID:37552724 | PMC:PMC9128018 | DOI:10.1021/acsestwater.2c00059

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Nevin Manimala Statistics

Localized and Whole-Room Effects of Portable Air Filtration Units on Aerosol Particle Deposition and Concentration in a Classroom Environment

ACS ES T Eng. 2022 Feb 17:acsestengg.1c00321. doi: 10.1021/acsestengg.1c00321. Online ahead of print.

ABSTRACT

In indoor environments with limited ventilation, recirculating portable air filtration (PAF) units may reduce COVID-19 infection risk via not only the direct aerosol route (i.e., inhalation) but also via an indirect aerosol route (i.e., contact with the surface where aerosol particles deposited). We systematically investigated the impact of PAF units in a mock classroom, as a supplement to background ventilation, on localized and whole-room surface deposition and particle concentration. Fluorescently tagged particles with a volumetric mean diameter near 2 μm were continuously introduced into the classroom environment via a breathing simulator with a prescribed inhalation-exhalation waveform. Deposition velocities were inferred on >50 horizontal and vertical surfaces throughout the classroom, while aerosol concentrations were spatially monitored via optical particle spectrometry. Results revealed a particle decay rate consistent with expectations based upon the reported clean air delivery rates of the PAF units. Additionally, the PAF units reduced peak concentrations by a factor of around 2.5 compared to the highest concentrations observed and led to a statistically significant reduction in deposition velocities for horizontal surfaces >2.5 m from the aerosol source. Our results not only confirm that PAF units can reduce particle concentrations but also demonstrate that they may lead to reduced particle deposition throughout an indoor environment when properly positioned with respect to the location of the particle source(s) within the room (e.g., where the largest group of students sit) and the predominant air distribution profile of the room.

PMID:37552723 | PMC:PMC8864773 | DOI:10.1021/acsestengg.1c00321

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Nevin Manimala Statistics

Quantitative Reverse Transcription PCR Surveillance of SARS-CoV-2 Variants of Concern in Wastewater of Two Counties in Texas, United States

ACS ES T Water. 2022 Jul 6:acsestwater.2c00103. doi: 10.1021/acsestwater.2c00103. Online ahead of print.

ABSTRACT

After its emergence in late November/December 2019, the severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2) rapidly spread globally. Recognizing that this virus is shed in feces of individuals and that viral RNA is detectable in wastewater, testing for SARS-CoV-2 in sewage collections systems has allowed for the monitoring of a community’s viral burden. Over a 9 month period, the influents of two regional wastewater treatment facilities were concurrently examined for wild-type SARS-CoV-2 along with variants B.1.1.7 and B.1.617.2 incorporated as they emerged. Epidemiological data including new confirmed COVID-19 cases and associated hospitalizations and fatalities were tabulated within each location. RNA from SARS-CoV-2 was detectable in 100% of the wastewater samples, while variant detection was more variable. Quantitative reverse transcription PCR (RT-qPCR) results align with clinical trends for COVID-19 cases, and increases in COVID-19 cases were positively related with increases in SARS-CoV-2 RNA load in wastewater, although the strength of this relationship was location specific. Our observations demonstrate that clinical and wastewater surveillance of SARS-CoV-2 wild type and constantly emerging variants of concern can be combined using RT-qPCR to characterize population infection dynamics. This may provide an early warning for at-risk communities and increases in COVID-19 related hospitalizations.

PMID:37552718 | PMC:PMC9291321 | DOI:10.1021/acsestwater.2c00103

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Nevin Manimala Statistics

Comparison of machine learning models to predict long-term outcomes after severe traumatic brain injury

Neurosurg Focus. 2023 Jun;54(6):E14. doi: 10.3171/2023.3.FOCUS2376.

ABSTRACT

OBJECTIVE: An estimated 1.5 million people die every year worldwide from traumatic brain injury (TBI). Physicians are relatively poor at predicting long-term outcomes early in patients with severe TBI. Machine learning (ML) has shown promise at improving prediction models across a variety of neurological diseases. The authors sought to explore the following: 1) how various ML models performed compared to standard logistic regression techniques, and 2) if properly calibrated ML models could accurately predict outcomes up to 2 years posttrauma.

METHODS: A secondary analysis of a prospectively collected database of patients with severe TBI treated at a single level 1 trauma center between November 2002 and December 2018 was performed. Neurological outcomes were assessed at 3, 6, 12, and 24 months postinjury with the Glasgow Outcome Scale. The authors used ML models including support vector machine, neural network, decision tree, and naïve Bayes models to predict outcome across all 4 time points by using clinical information available on admission, and they compared performance to a logistic regression model. The authors attempted to predict unfavorable versus favorable outcomes (Glasgow Outcome Scale scores of 1-3 vs 4-5), as well as mortality. Models’ performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) with 95% confidence interval and balanced accuracy.

RESULTS: Of the 599 patients in the database, the authors included 501, 537, 469, and 395 at 3, 6, 12, and 24 months posttrauma. Across all time points, the AUCs ranged from 0.71 to 0.85 for mortality and from 0.62 to 0.82 for unfavorable outcomes with various modeling strategies. Decision tree models performed worse than all other modeling approaches for multiple time points regarding both unfavorable outcomes and mortality. There were no statistically significant differences between any other models. After proper calibration, the models had little variation (0.02-0.05) across various time points.

CONCLUSIONS: The ML models tested herein performed with equivalent success compared with logistic regression techniques for prognostication in TBI. The TBI prognostication models could predict outcomes beyond 6 months, out to 2 years postinjury.

PMID:37552699 | DOI:10.3171/2023.3.FOCUS2376

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Nevin Manimala Statistics

Prevalence and correlates associated with early childbearing among teenage girls in Ethiopia: A multilevel analysis

PLoS One. 2023 Aug 8;18(8):e0289102. doi: 10.1371/journal.pone.0289102. eCollection 2023.

ABSTRACT

BACKGROUND: Teenage childbearing remains a significant global health concern, and in nations with limited resources, it is the major cause of newborn and maternal deaths. Early teenage childbearing is still Ethiopia’s public health issue. Therefore, the goal of this study was to identify the prevalence and correlates of influencing early childbearing among teenage girls across Ethiopia.

METHODS: We conducted a secondary analysis of cross-sectional data from the 2016 Ethiopian Demographic and Health Survey. A multistage stratified cluster sampling strategy based on the community was used to include the 3,498 participants in total. To determine the significantly correlated factors that influence adolescent pregnancy, a multilevel binary logistic regression analysis was used. The factors that have a significant association with early childbearing were identified using the Adjusted Odds Ratio (AOR) and 95% Confidence Interval (CI).

RESULTS: This study demonstrated that 10.3% of teens across the country had children at an early age. The odds of early childbearing among teenage girls increased with first marriages occurring before the age of 18, non-formal education, being from a lower- or middle-class family, not using contraceptives, following Muslim or other religious beliefs, and being aware of the fertile window. Teenagers who had exposure to the media, however, had a reduced chance of becoming pregnant early.

CONCLUSIONS: The study indicates that early teenage childbearing is still Ethiopia’s most significant public health problem. Therefore, the Ethiopian government should ban early marriage while also taking steps to reduce the risk through formal education, improved access to reproductive health education, and contraception, particularly for adolescent girls from low-income families and, by educating religious institutions about pregnancy dangers.

PMID:37552698 | DOI:10.1371/journal.pone.0289102

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Nevin Manimala Statistics

Integrated brain and plasma dual-channel metabolomics to explore the treatment effects of Alpinia oxyphyllaFructus on Alzheimer’s disease

PLoS One. 2023 Aug 8;18(8):e0285401. doi: 10.1371/journal.pone.0285401. eCollection 2023.

ABSTRACT

Alpinia oxyphylla Fructus, called Yizhi in Chinese, is the dried fruit of Alpinia oxyphylla Miquel. It has been used in traditional Chinese medicine to treat dementia and memory defects of Alzheimer’s disease for many years. However, the underlying mechanism is still unclear. In this study, we used a rat Alzheimer’s disease model on intrahippocampal injection of aggregated Aβ1-42 to study the effects of Alpinia oxyphylla Fructus. A brain and plasma dual-channel metabolomics approach combined with multivariate statistical analysis was further performed to determine the effects of Alpinia oxyphylla Fructus on Alzheimer’s disease animals. As a result, in the Morris water maze test, Alpinia oxyphylla Fructus had a clear ability to ameliorate the impaired learning and memory of Alzheimer’s disease rats. 11 differential biomarkers were detected in AD rats’ brains. The compounds mainly included amino acids and phospholipids; after Alpinia oxyphylla Fructus administration, 9 regulated biomarkers were detected compared with the AD model group. In the plasma of AD rats, 29 differential biomarkers, primarily amino acids, phospholipids and fatty acids, were identified; After administration, 23 regulated biomarkers were detected. The metabolic pathways of regulated metabolites suggest that Alpinia oxyphylla Fructus ameliorates memory and learning deficits in AD rats principally by regulating amino acid metabolism, lipids metabolism, and energy metabolism. In conclusion, our results confirm and enhance our current understanding of the therapeutic effects of Alpinia oxyphylla Fructus on Alzheimer’s disease. Meanwhile, our work provides new insight into the potential intervention mechanism of Alpinia oxyphylla Fructus for Alzheimer’s disease treatment.

PMID:37552694 | DOI:10.1371/journal.pone.0285401

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Nevin Manimala Statistics

The influence of superstitions and emotions on villagers’ attitudes towards striped hyena in southwestern Iran

PLoS One. 2023 Aug 8;18(8):e0285546. doi: 10.1371/journal.pone.0285546. eCollection 2023.

ABSTRACT

The intensity of human-carnivore conflict in socio-ecological systems may primarily be determined by people’s attitudes and perceptions of carnivore-related threats. Direct or indirect threats posed by large carnivores to human interests may eventually lead to negative attitudes that can trigger retaliatory bahaviour against them. We studied local people’s attitudes towards striped hyena (Hyaena hyaena), the nature and extent of the human-hyena conflict, and the socio-cultural drivers of the conflicts in 19 rural communities in southwestern Iran. We employed structural equation modelling to assess socio-cultural factors affecting attitudes towards striped hyenas. The findings of 300 interviews showed significant differences in local people’s superstitious attitudes regarding gender, age, and education. More than 40% of the participants had encountered hyenas, and on average, each respondent lost 0.44 livestock in the past five years due to hyena attacks. However, livestock depredation by the hyena was low (13.3%) compared to the damage inflicted by all carnivores (73%). While the respondents indicated some degrees of fear, hatred to hyena was relatively low and they generally showed positive attitudes towards the species. Women and older people expressed the highest and respondents with higher education the least superstitious beliefs. Attitude score of respondents toward hyenas was correlated negatively with hatred for hyenas and positively with knowledge about them, but socio-demographics effects on attitudes towards hyenas were not statistically significant. Self-reported livestock loss was a relatively good predictor of hatred and fear. Herders who had not protected their livestock reported carnivore attacks at least once. We conclude that superstitions can potentially negatively affect hyena persistence, but can be reduced by improving the educational level of local people.

PMID:37552693 | DOI:10.1371/journal.pone.0285546

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Nevin Manimala Statistics

Suicide after leaving the UK Armed Forces 1996-2018: A cohort study

PLoS Med. 2023 Aug 8;20(8):e1004273. doi: 10.1371/journal.pmed.1004273. eCollection 2023 Aug.

ABSTRACT

BACKGROUND: There are comparatively few international studies investigating suicide in military veterans and no recent UK-wide studies. This is important because the wider context of being a UK Armed Forces (UKAF) veteran has changed in recent years following a period of intensive operations. We aimed to investigate the rate, timing, and risk factors for suicide in personnel who left the UKAF over a 23-year period.

METHODS AND FINDINGS: We carried out a retrospective cohort study of suicide in personnel who left the regular UKAF between 1996 and 2018 linking national databases of discharged personnel and suicide deaths, using survival analysis to examine the risk of suicide in veterans compared to the general population and conditional logistic regression to investigate factors most strongly associated with suicide after discharge. The 458,058 individuals who left the UKAF accumulated over 5,852,100 person years at risk, with a median length of follow-up of 13 years, were mostly male (91%), and had a median age of 26 years at discharge. 1,086 (0.2%) died by suicide. The overall rate of suicide in veterans was slightly lower than the general population (standardised mortality ratio, SMR [95% confidence interval, CI] 94 [88 to 99]). However, suicide risk was 2 to 3 times higher in male and female veterans aged under 25 years than in the same age groups in the general population (age-specific mortality ratios ranging from 160 to 409). Male veterans aged 35 years and older were at reduced risk of suicide (age-specific mortality ratios 47 to 80). Male sex, Army service, discharge between the ages of 16 and 34 years, being untrained on discharge, and length of service under 10 years were associated with higher suicide risk. Factors associated with reduced risk included being married, a higher rank, and deployment on combat operations. The rate of contact with specialist NHS mental health services (273/1,086, 25%) was lowest in the youngest age groups (10% for 16- to 19-year-olds; 23% for 20- to 24-year-olds). Study limitations include the fact that information on veterans was obtained from administrative databases and the role of pre-service vulnerabilities and other factors that may have influenced later suicide risk could not be explored. In addition, information on contact with support services was only available for veterans in contact with specialist NHS mental health services and not for those in contact with other health and social care services.

CONCLUSIONS: In this study, we found suicide risk in personnel leaving the UKAF was not high but there are important differences according to age, with higher risk in young men and women. We found a number of factors which elevated the risk of suicide but deployment was associated with lower risk. The focus should be on improving and maintaining access to mental health care and social support for young service leavers, as well as implementing general suicide prevention measures for all veterans regardless of age.

PMID:37552686 | DOI:10.1371/journal.pmed.1004273