Background
Reduced pelvic limb reflexes in dogs with spinal cord injury typically suggests a lesion of the L4‐S3 spinal cord segments. However, pelvic limb reflexes might also be reduced in dogs with ...a T3‐L3 myelopathy and concurrent spinal shock.
Hypothesis/Objectives
We hypothesized that statistical models could be used to identify clinical variables associated with spinal shock in dogs with spinal cord injuries.
Animals
Cohort of 59 dogs with T3‐L3 myelopathies and spinal shock and 13 dogs with L4‐S3 myelopathies.
Methods
Data used for this study were prospectively entered by partner institutions into the International Canine Spinal Cord Injury observational registry between October 2016 and July 2019. Univariable logistic regression analyses were performed to assess the association between independent variables and the presence of spinal shock. Independent variables were selected for inclusion in a multivariable logistic regression model if they had a significant effect (P ≤ .1) on the odds of spinal shock in univariable logistic regression.
Results
The final multivariable model included the natural log of weight (kg), the natural log of duration of clinical signs (hours), severity (paresis vs paraplegia), and pelvic limb tone (normal vs decreased/absent). The odds of spinal shock decreased with increasing weight (odds ratio OR = 0.28, P = .09; confidence interval CI 0.07‐1.2), increasing duration (OR = 0.44, P = .02; CI 0.21‐0.9), decreased pelvic limb tone (OR = 0.04, P = .003; CI 0.01‐0.36), and increased in the presence of paraplegia (OR = 7.87, P = .04; CI 1.1‐56.62).
Conclusions and Clinical Importance
A formula, as developed by the present study and after external validation, could be useful for assisting clinicians in determining the likelihood of spinal shock in various clinical scenarios and aid in diagnostic planning.
The dynamics of infectious diseases are greatly influenced by the movement of both susceptible and infected hosts. To accurately represent disease dynamics among a mobile host population, detailed ...movement models have been coupled with disease transmission models. However, a number of different host movement models have been proposed, each with their own set of assumptions and results that differ from the other models. Here, we compare two movement models coupled to the same disease transmission model using network analyses. This application of network analysis allows us to evaluate the fit and accuracy of the movement model in a multilevel modeling framework with more detail than established statistical modeling fitting methods. We used data that detailed mobile pastoralists' movements as input for 100 stochastic simulations of a Spatio-Temporal Movement (STM) model and 100 stochastic simulations of an Individual Movement Model (IMM). Both models represent dynamic movement and subsequent contacts. We generated networks in which nodes represent camps and edges represent the distance between camps. We simulated pathogen transmission over these networks and tested five network metrics-strength, betweenness centrality, three-step reach, density, and transitivity-to determine which could predict disease simulation outcomes and thereby be used to correlate model simulation results with disease transmission simulations. We found that strength, network density, and three-step reach of movement model results correlated with the final epidemic size of outbreak simulations. Betweenness centrality only weakly correlated for the IMM model. Transitivity only weakly correlated for the STM model and time-varying IMM model metrics. We conclude that movement models coupled with disease transmission models can affect disease transmission results and should be carefully considered and vetted when modeling pathogen spread in mobile host populations. Strength, network density, and three-step reach can be used to evaluate movement models before disease simulations to predict final outbreak sizes. These findings can contribute to the analysis of multilevel models across systems.
This article used ethnographic methods to examine how Ohio hunters' knowledge, attitudes, and practices affect risk exposure to infectious disease. Semi-structured interviews were conducted with ...hunters from Southeast Ohio, and an online survey was distributed to a random sample of licensed Ohio hunters. Data analyses indicated that Ohio hunters learn about wildlife disease through word-of-mouth, hunting publications, and online sources. They perceived low-to-no risk of exposure to infectious diseases. Although hunters were generally knowledgeable about infectious wildlife diseases, they were more concerned about the impact on wildlife populations than their own health. The results contribute to a better understanding of the role of hunter behavior in response to disease events, the identification of future interventions that would most effectively inform hunters about wildlife diseases, and how to minimize their risk of exposure.
Abstract
Background
Early life plays a vital role in the development of the gut microbiome and subsequent health. While many factors that shape the gut microbiome have been described, including ...delivery mode, breastfeeding, and antibiotic use, the role of household environments is still unclear. Furthermore, the development of the gut antimicrobial resistome and its role in health and disease is not well characterized, particularly in settings with water insecurity and less sanitation infrastructure.
Results
This study investigated the gut microbiome and resistome of infants and young children (ages 4 days-6 years) in rural Nicaragua using Oxford Nanopore Technology’s MinION long-read sequencing. Differences in gut microbiome diversity and antibiotic resistance gene (ARG) abundance were examined for associations with host factors (age, sex, height for age z-score, weight for height z-score, delivery mode, breastfeeding habits) and household environmental factors (animals inside the home, coliforms in drinking water, enteric pathogens in household floors, fecal microbial source tracking markers in household floors). We identified anticipated associations of higher gut microbiome diversity with participant age and vaginal delivery. However, novel to this study were the significant, positive associations between ruminant and dog fecal contamination of household floors and gut microbiome diversity. We also identified greater abundance of potential pathogens in the gut microbiomes of participants with higher fecal contamination on their household floors. Path analysis revealed that water quality and household floor contamination independently and significantly influenced gut microbiome diversity when controlling for age. These gut microbiome contained diverse resistome, dominated by multidrug, tetracycline, macrolide/lincosamide/streptogramin, and beta-lactam resistance. We found that the abundance of ARGs in the gut decreased with age. The bacterial hosts of ARGs were mainly from the family
Enterobacteriaceae
, particularly
Escherichia coli
.
Conclusions
This study identified the role of household environmental contamination in the developing gut microbiome and resistome of young children and infants with a One Health perspective. We found significant relationships between host age, gut microbiome diversity, and the resistome. Understanding the impact of the household environment on the development of the resistome and microbiome in early life is essential to optimize the relationship between environmental exposure and human health.
Graphical Abstract
Modeling the movements of humans and animals is critical to understanding the transmission of infectious diseases in complex social and ecological systems. In this paper, we focus on the movements of ...pastoralists in the Far North Region of Cameroon, who follow an annual transhumance by moving between rainy and dry season pastures. Describing, summarizing, and modeling the transhumance movements in the region are important steps for understanding the role these movements may play in the transmission of infectious diseases affecting humans and animals. We collected data on this transhumance system for four years using a combination of surveys and GPS mapping. An analysis on the spatial and temporal characteristics of pastoral mobility suggests four transhumance modes, each with its own properties. Modes M1 and M2 represent the type of transhumance movements where pastoralists settle in a campsite for a relatively long period of time (≥20 days) and then move around the area without specific directions within a seasonal grazing area. Modes M3 and M4 on the other hand are the situations when pastoralists stay in a campsite for a relatively short period of time (<20 days) when moving between seasonal grazing areas. These four modes are used to develop a spatial-temporal mobility (STM) model that can be used to estimate the probability of a mobile pastoralist residing at a location at any time. We compare the STM model with two reference models and the experiments suggest that the STM model can effectively capture and predict the space-time dynamics of pastoral mobility in our study area.
In this article, we consider the implications of Murray Last's (1981) Knowing About Not Knowing for the study of ethnoveterinary knowledge of mobile pastoralists in the Far North Region of Cameroon. ...Specifically, we ask two interrelated questions: (1) what is the nature of this knowledge, and (2) what is the best way to study it? We conducted a study of pastoralists' knowledge of human and animal infectious diseases to evaluate the claim that mobile pastoralists in the Chad Basin do not have a concept for zoonotic diseases. We used a combination of free lists and semi-structured interviews to study pastoralists' knowledge. The results suggest that pastoralists do not have a concept for zoonotic diseases. Moreover, we found considerable variation in pastoralists' ethnoveterinary knowledge and examples of not knowing, which contrasts with previous studies that do not describe much variation in ethnoveterinary knowledge. In our discussion, we consider to what extent descriptions of ethnoveterinary knowledge are the product of researchers' conceptual framework and methodology.
We describe a method for analyzing the within-household network dynamics of a disease transmission. We apply it to analyze the occurrences of endemic diarrheal disease in Cameroon, Central Africa ...based on observational, cross-sectional data available from household health surveys.
To analyze the data, we apply formalism of the dynamic SID (susceptible-infected-diseased) process that describes the disease steady-state while adjusting for the household age-structure and environment contamination, such as water contamination. The SID transmission rates are estimated via MCMC method with the help of the so-called synthetic likelihood approach.
The SID model is fitted to a dataset on diarrhea occurrence from 63 households in Cameroon. We show that the model allows for quantification of the effects of drinking water contamination on both transmission and recovery rates for household diarrheal disease occurrence as well as for estimation of the rate of silent (unobserved) infections.
The new estimation method appears capable of genuinely capturing the complex dynamics of disease transmission across various human, animal and environmental compartments at the household level. Our approach is quite general and can be used in other epidemiological settings where it is desirable to fit transmission rates using cross-sectional data.
The R-scripts for carrying out the computational analysis described in the paper are available at https://github.com/cbskust/SID.
OBJECTIVE To assess the discriminatory value for corticosteroid-induced alkaline phosphatase (CiALP) activity and other variables that can be measured routinely on a CBC and biochemical analysis for ...the diagnosis of hypoadrenocorticism in dogs. SAMPLE Medical records of 57 dogs with confirmed hypoadrenocorticism and 57 control dogs in which hypoadrenocorticism was suspected but ruled out. PROCEDURES A retrospective case-control study was conducted. Dogs were included if a CBC and complete biochemical analysis had been performed. Dogs with iatrogenic hypoadrenocorticism and dogs treated previously with glucocorticoids were excluded. Cortisol concentration for dogs with hypoadrenocorticism was ≤ 2 μg/dL both before and after ACTH administration. Cortisol concentration for control dogs was > 4 μg/dL before or after ACTH administration. RESULTS Area under the receiver operating characteristic (ROC) curve for CiALP activity was low (0.646; 95% confidence interval, 0.494 to 0.798). Area under the ROC curve for a model that combined the CiALP activity, Na-to-K ratio, eosinophil count, activity of creatine kinase, and concentrations of SUN and albumin was high (0.994; 95% confidence interval, 0.982 to 1.000). Results for this model could be used to correctly classify all dogs, except for 1 dog with hypoadrenocorticism and no electrolyte abnormalities. CONCLUSIONS AND CLINICAL RELEVANCE CiALP activity alone cannot be used as a reliable diagnostic test for hypoadrenocorticism in dogs. Combined results for CiALP activity, Na-to-K ratio, eosinophil count, creatine kinase activity, and concentrations of SUN and albumin provided an excellent means to discriminate between hypoadrenocorticism and diseases that mimic hypoadrenocorticism.
Foot-and-mouth disease (FMD) affects cloven-hoofed livestock and agricultural economies worldwide. Analyses of the 2001 FMD outbreak in the United Kingdom informed how livestock movement contributed ...to disease spread. However, livestock reared in other locations use different production systems that might also influence disease dynamics. Here, we investigate a livestock production system known as transhumance, which is the practice of moving livestock between seasonal grazing areas. We built mechanistic models using livestock movement data from the Far North Region of Cameroon. We represented these data as a dynamic network over which we simulated disease transmission and examined three questions. First, we asked what were characteristics of simulated FMDV transmission across a transhumant pastoralist system. Second, we asked how simulated FMDV transmission across a transhumant pastoralist system differed from transmission across this same population held artificially stationary, thereby revealing the effect of movement on disease dynamics. Third, we asked if disease simulations on well-studied theoretical networks are similar to disease simulations on this empirical dynamic network. The results show that the empirical dynamic network was sparsely connected except for an eight-week period in September and October when pastoralists move from rainy season to dry season grazing areas. The mean epidemic size across all 3,744 simulations was 99.9% and the mean epidemic duration was 1.45 years. Disease simulations across the static network showed a smaller mean epidemic size (27.6%) and a similar epidemic duration (1.5 years). Epidemics simulated on theoretical networks showed similar final epidemic sizes (100%) and different mean durations. Our simulations indicate that transhumant livestock systems have the potential to host FMDV outbreaks that affect almost all livestock and last longer than a year. Furthermore, our comparison of empirical and theoretical networks underscores the importance of using empirical data to understand the role of mobility in the transmission of infectious diseases.
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