Seroprevalence of SARS-CoV-2 Antibodies Among Rural Healthcare Workers

The objective of this longitudinal cohort study was to determine the seroprevalence of antibodies to severe acute respiratory syndrome coronavirus-2 (SARSCoV- 2) in healthcare workers employed at healthcare clinics in three rural counties in eastern South Dakota and western Minnesota from May 13, 2020 through December 22, 2020. Three blood draws were performed at five clinical sites and tested for the presence of antibodies against the SARS-CoV-2 virus. Serum samples were tested for the presence of antibodies using a fluorescent microsphere immunoassay (FMIA), neutralization of SARS-CoV-2 Spike-pseudotyped particles (SARS-CoV-2pp) assay, and serum virus neutralization (SVN) assay. The seroprevalence was determined to be 1/336 (0.29%) for samples collected from 5/13/20-7/13/20, 5/260 (1.92%) for samples collected from 8/13/20-9/25/20, and 35/235 (14.89%) for samples collected from 10/16/20-12/22/20. Eight of the 35 (22.8%) seropositive individuals identified in the final draw did not report a previous diagnosis with COVID-19. There was a high correlation (\u3e90%) among the FMIA and virus neutralization assays. Each clinical site\u27s seroprevalence was higher than the cumulative incidence for the general public in each respective county as reported by state public health agencies. As of December 2020, there was a high percentage (85%) of seronegative individuals in the study population

Bayesian spatio-temporal analysis and geospatial risk factors of Human Monocytic Ehrlichiosis

Citation: Raghavan RK, Neises D, Goodin DG, Andresen DA, Ganta RR (2014) Bayesian Spatio-Temporal Analysis and Geospatial Risk Factors of Human Monocytic Ehrlichiosis. PLoS ONE 9(7): e100850. doi:10.1371/journal.pone.0100850Variations in spatio-temporal patterns of Human Monocytic Ehrlichiosis (HME) infection in the state of Kansas, USA were examined and the relationship between HME relative risk and various environmental, climatic and socio-economic variables were evaluated. HME data used in the study was reported to the Kansas Department of Health and Environment between years 2005–2012, and geospatial variables representing the physical environment [National Land cover/Land use, NASA Moderate Resolution Imaging Spectroradiometer (MODIS)], climate [NASA MODIS, Prediction of Worldwide Renewable Energy (POWER)], and socio-economic conditions (US Census Bureau) were derived from publicly available sources. Following univariate screening of candidate variables using logistic regressions, two Bayesian hierarchical models were fit; a partial spatio-temporal model with random effects and a spatio-temporal interaction term, and a second model that included additional covariate terms. The best fitting model revealed that spatio-temporal autocorrelation in Kansas increased steadily from 2005–2012, and identified poverty status, relative humidity, and an interactive factor, ‘diurnal temperature range x mixed forest area’ as significant county-level risk factors for HME. The identification of significant spatio-temporal pattern and new risk factors are important in the context of HME prevention, for future research in the areas of ecology and evolution of HME, and as well as climate change impacts on tick-borne diseases

(±)-syn-Isopropyl 4-(1,1,1,3,3,3-hexa­fluoro­propan-2-yl­oxy)-1-hydr­oxy-3-methyl-2-(prop-1-yn­yl)cyclo­pent-2-ene­carboxyl­ate

The title compound, C16H18F6O4, was obtained through an unprecedented one-pot reaction sequence involving a Gosteli–Claisen rearrangement and a cyclo­isomerization. The constitution and relative configuration were determined by single-crystal X-ray diffraction analysis. In the crystal, mol­ecules are connected via O—H ⋯ O hydrogen bonds

Hierarchical Bayesian Spatio-Temporal Analysis of Climatic and Socio-Economic Determinants of Rocky Mountain Spotted Fever

Citation: Raghavan, R. K., Goodin, D. G., Neises, D., Anderson, G. A., & Ganta, R. R. (2016). Hierarchical Bayesian Spatio-Temporal Analysis of Climatic and Socio-Economic Determinants of Rocky Mountain Spotted Fever. Plos One, 11(3), 17. doi:10.1371/journal.pone.0150180This study aims to examine the spatio-temporal dynamics of Rocky Mountain spotted fever (RMSF) prevalence in four contiguous states of Midwestern United States, and to determine the impact of environmental and socio-economic factors associated with this disease. Bayesian hierarchical models were used to quantify space and time only trends and spatio-temporal interaction effect in the case reports submitted to the state health departments in the region. Various socio-economic, environmental and climatic covariates screened a priori in a bivariate procedure were added to a main-effects Bayesian model in progressive steps to evaluate important drivers of RMSF space-time patterns in the region. Our results show a steady increase in RMSF incidence over the study period to newer geographic areas, and the posterior probabilities of county-specific trends indicate clustering of high risk counties in the central and southern parts of the study region. At the spatial scale of a county, the prevalence levels of RMSF is influenced by poverty status, average relative humidity, and average land surface temperature (> 35 degrees C) in the region, and the relevance of these factors in the context of climate-change impacts on tick-borne diseases are discussed

The effects of balance exercises on lower extremity injury prevention: a systematic review

Background: Previous systematic reviews have identified injury risk predictors of sport-related lower extremity (LE) injuries based on exercise programs. There are limited studies indicating the advantage of single-leg balance (SLB) training and its effectiveness in lowering injury rates. Research Question: Investigate which types of balance training were used in effective injury prevention programs. Methods: A systematic review utilizing the SPORTDiscus and PubMed databases. Inclusion criteria were randomized controlled trials from the last 10 years that included LE injury prevention, a balance training component and participants aged 10-30. Results: 5 studies were included in the qualitative synthesis, comprising 2,926 athletes. Three studies included SLB on firm surfaces, none significantly reduced injury. Two studies had SLB on alternating surfaces, one showed a significant reduction in injury risk. One article included SLB with psychomotor and attentional demands, which reduced injury risk. Two studies including SLB with dynamic movement showed reduced injury risk. Two studies including SLB with perturbations showed reduced injury risk and three studies showed that high adherence led to decreased injury risk. Clinical Significance: There is a dearth of research in this area, with only 5 studies within the last 10 years, so caution needs to be applied to make clinical judgments based on this systematic review. The current studies show a trend toward SLB training on firm surfaces not providing any protection against injury. However, SLB training that is incorporated with dual tasks, dynamic movement, or perturbations and that have high adherence, may give some benefit toward injury reduction

Evaluating spatial surveillance: detection of known outbreaks in real data

Since the anthrax attacks of October 2001 and the SARS outbreaks of recent years, there has been an increasing interest in developing surveillance systems to aid in the early detection of such illness. Systems have been established which do this is by monitoring primary health-care visits, pharmacy sales, absenteeism records, and other non-traditional sources of data. While many resources have been invested in establishing such systems, relatively little effort has as yet been expended in evaluating their performance. One way to evaluate a given surveillance system is to compare the signals it generates with known outbreaks identified in other systems. In public health practice, for example, public health departments investigate reports of illness and sometimes track hospital admissions. Comparison of new systems with extant systems cannot generate estimates of test characteristics such as sensitivity and specificity, since the actual number of positives and negatives cannot be known. However, the comparison can reveal whether a new or proposed system’s signals match outbreaks detected by the existing system. This could help support or reject the new system as an alternative or complement to the extant system. We propose three methods to test the null hypothesis that the new system does not signal true outbreaks more often than would be expected by chance. The methods dier in the restrictiveness of the assumptions required. Each test may detect weaknesses in the new system, depending on the distribution of outbreaks and can be used to construct confidence limits on the agreement between the new system’s signals and the outbreaks, given the distribution of the signals. They can be used to assess whether the new system works in that it detects the outbreaks better than chance would suggest and can also determine if the new systems’ signals are generated earlier than an extant system