Investigation of factors associated with the probability of racehorses being pulled up in steeplechase races at Cheltenham racetrack

2012 ◽  
Vol 8 (2) ◽  
pp. 95-101 ◽  
Author(s):  
F. Mata ◽  
J. Williams ◽  
F. Marks
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Predictive Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Influence ◽  
Factors Associated ◽  
Binomial Regression

Limited research has been conducted to investigate the risk factors associated with horses being pulled up in steeplechase races. The aim of this study was to identify risk factors associated with pulled up horses in steeplechase races at Cheltenham racecourse and utilise these to propose preventative strategies to reduce prospective risks of racehorses being pulled up in steeplechase races. Horse and racetrack factors that could be associated with an increased chance of horses being pulled up, extrapolated from previous research into racehorse falls and clinical injury, were identified and collated via the Racingpost website for all steeplechases (n=1,032) at Cheltenham for a 21 year period (January 1990 - December 2010). A logistic regression was used to model the probability of existence of pulled up horses in a given race. A negative binomial regression was used to model the number of pulled up horses in a given race. Increasing numbers of runners (P<0.001) starting a race and races of longer distances (P<0.001) resulted in more pulled up horses within the race. In contrast, faster race speeds (P<0.01) were associated with the presence of less pulled up horses in a race. Each additional m/s in the speed of the horses running the race in race results in a decreased probability of 38.1% that the race will contain pulled up horses. The influence of other horses within steeplechase races at Cheltenham appears to effect speed within racing and can exert a positive or negative influence on how many horses are pulled up in a race. It is suggested that additional co-variant factors such as going and distance can also impact upon speed, and that it is the interaction of these variables that produce equine fatigue resulting in pulled up horses. The predictive models devised have the potential to be employed to assess risk of horses being pulled up for other racetracks.

scholarly journals Loneliness, physical activity and mental health during Covid-19: a longitudinal analysis of depression and anxiety between 2015 and 2020

2020 ◽  
Author(s):  
Byron Creese ◽  
Zunera Khan ◽  
William Henley ◽  
Siobhan O’Dwyer ◽  
Anne Corbett ◽  
...  
Keyword(s):  
Mental Health ◽  
Physical Activity ◽  
Risk Factors ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Annual Data ◽  
Activity Levels ◽  
Binomial Regression ◽  
Mental Health Questionnaire ◽  
The Impact

BackgroundLoneliness and physical activity are important targets for research into the impact of COVID-19 because they have established links with mental health, could be exacerbated by social distancing policies and are potentially modifiable.MethodWe analysed mental health data collected during COVID-19 from adults aged 50 and over alongside comparable annual data collected between 2015 and 2019 from the same sample. Trajectories of depression (PHQ-9) and anxiety (GAD-7) were analysed with respect to loneliness, physical activity levels and a number of socioeconomic and demographic characteristics using zero-inflated negative binomial regression.Results3,281 people completed the COVID-19 mental health questionnaire, all had at least one data point prior to 2020. In 2020, the adjusted PHQ-9 score for loneliness was 3.2. (95% CI: 3.0-3.4), an increase of one point on previous years and 2 points higher than people not rated lonely, whose score did not change in 2020 (1.2, 95% CI: 1.1-1.3). PHQ-9 was 2.6 (95% CI: 2.4-2.8) in people with decreased physical activity, an increase of 0.5 on previous years. In contrast, PHQ-9 in 2020 for people whose physical activity had not decreased was 1.7 (95% CI: 1.6-1.8), similar to previous years. A similar relationship was observed for GAD-7 though the differences were smaller and the absolute burden of symptoms lower.ConclusionsAfter accounting for pre-COVID-19 trends, we show that experiencing loneliness and decreased physical activity are risk factors for worsening mental health during the pandemic. Our findings highlight the need to examine policies which target these potentially modifiable risk factors.

scholarly journals Injustices in pandemic vulnerability: A spatial-statistical analysis of the CDC Social Vulnerability Index and COVID-19 outcomes in the U.S.

2021 ◽  
Author(s):  
Jamie Song ◽  
Douglas Wiebe ◽  
Sara Solomon ◽  
Eugenia South
Keyword(s):  
Logistic Regression ◽  
Social Vulnerability ◽  
Negative Binomial ◽  
Hot Spot ◽  
Negative Binomial Regression ◽  
Vulnerability Index ◽  
Social Vulnerability Index ◽  
Binomial Regression ◽  
Average Annual Temperature ◽  
The U.S

Background: The COVID-19 pandemic has exacerbated health injustices in the U.S. driven by racism and other forms of structural violence. Research has shown the disproportionate impacts of COVID-19 morbidity and mortality in the most marginalized communities. Objectives: We examined the associations between COVID-19 cumulative incidence (CI) and case-fatality risk (CFR) and the CDC's Social Vulnerability Index (SVI), a composite score assessing historical marginalization and thus vulnerability to disaster events. Methods: Using county-level data from national databases, we used population density, Gini index, percent uninsured, and average annual temperature as covariates, and employed negative binomial regression to evaluate relationships between SVI and COVID-19 outcomes. Optimized hot spot analysis identified hot spots of COVID-19 CI and CFR, which were compared in terms of SVI using logistic regression. Results: As of 2/3/21, 26,452,031 cases of and 448,786 deaths from COVID-19 had been reported in the U.S. Negative binomial regression showed that counties in the top SVI quintile reported 13.7% higher CI (p<0.001) than those in the bottom SVI quintile. Additionally, each unit increase in a county's SVI score was associated with a 0.2% increase in CFR (p<0.001). Logistic regression analysis showed that counties in the lowest SVI quintile had significantly greater odds of being in a CI hot spot than all other counties, yet counties in the highest SVI quintile had 63% greater odds (p=0.008) of being in a CFR hot spot than counties in the lowest SVI quintile. Conclusion: We demonstrated a significant relationship between SVI and CFR, but the relationship between SVI and CI is complex and warrants further investigation. SVI may help elucidate unequal impacts of COVID-19 and guide prioritization of vaccines to communities most impacted by structural injustices.

scholarly journals Risk Factors Influencing Cyberbullying Perpetration among Middle School Students in Korea: Analysis Using the Zero-Inflated Negative Binomial Regression Model

2021 ◽  
Vol 18 (5) ◽  
pp. 2224 ◽  
Author(s):  
Kyung Im Kang ◽  
Kyonghwa Kang ◽  
Chanhee Kim
Keyword(s):  
Risk Factors ◽  
Middle School ◽  
Middle School Students ◽  
Regression Model ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Model Analysis ◽  
Negative Binomial Regression Model ◽  
School Students ◽  
Binomial Regression

This cross-sectional descriptive study identified risk factors and predictors related to the perpetration of and potential for cyberbullying among adolescents, respectively. The analysis included a zero-inflated negative binomial regression model. Data were assessed from 2590 middle-school student panels obtained during the first wave of the Korean Child and Youth Panel Survey 2018. Of these respondents, 63.7% said they had not experienced the perpetration of cyberbullying. However, a subsequent count model analysis showed that several factors were significantly associated with cyberbullying, including offline delinquency, aggression, smartphone dependency, and smartphone usage on weekends (either 1–3 h or over 3 h). A logit model analysis also showed several predictive factors that increased the likelihood of cyberbullying, including gender (boys), offline delinquency, aggression, smartphone usage during weekdays (1–3 h), computer usage during weekends (1–3 h), and negative parenting. These identified risks and predictors should be useful for interventions designed to prevent the perpetration of cyberbullying among middle school students.

scholarly journals Clinical features of COVID-19 in Ghana: symptomatology, illness severity and comorbid non-communicable diseases

2020 ◽  
Vol 54 (4s) ◽  
pp. 23-32
Author(s):  
Alfred E Yawson ◽  
Ebenezer Oduro-Mensah ◽  
John Tetteh ◽  
Isaac Adomako ◽  
Evelyn Adjei-Mensah ◽  
...  
Keyword(s):  
Clinical Features ◽  
Negative Binomial ◽  
Communicable Disease ◽  
Negative Binomial Regression ◽  
Illness Severity ◽  
P Value ◽  
Factors Associated ◽  
Binomial Regression ◽  
History Of ◽  
Non Communicable Disease

Objective: This analysis described the clinical features of COVID-19 in the early phase of the pandemic in Ghana.Methods: Data were extracted from two national COVID-19 treatment centers in Ghana for over 11 weeks(from March to May 2020). Descriptive and inferential statistics were performed. Modified Ordered Logistic and Negative Binomial Regression analysis were applied to establish factors associated with illness severity and Non-communicable Disease (NCDs) counts respectively. All analysis was conducted at the 95% confidence level (p-value ≤ 0.05) using Stata 16.Results: Among the 275 patients, the average age was 40.7±16.4, with a preponderance of males (54.5%). The three commonest symptoms presented were cough (21.3%), headache (15.7%), and sore throat (11.7%). Only 7.6% of the patients had a history of fever. Most patients were asymptomatic (51.65). Approximately 38.9% have an underlying co-morbid NCDs, with Hypertension (32.1%), Diabetes (9.9%), and Asthma (5.2%) being the three commonest. The odds of Moderate/severe (MoS) was significantly higher for those with unknown exposures to similar illness [aOR(95%CI) = 4.27(1.12-10.2)] compared with non-exposure to similar illness. An increased unit of NCD’s count significantly increased the odds of COVID-19 MoS illness by 26%[cOR(95%CI) =1.26(1.09-1.84)] and 67% (adjusting for age) [aOR(95%CI)=1.67(1.13-2.49)].Conclusion: The presence of cardiovascular co-morbidities dictated the frequency of reported symptoms and severity of COVID-19 infection in this sample of Ghanaians. Physicians should be aware of the presence of co-morbid NCDs and prepare to manage effectively among COVID-19 patients.

scholarly journals Tick trails: the role of online recreational trail reviews in identifying risk factors and behavioral recommendations associated with tick encounters in Indiana

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kristina R. Anderson ◽  
Jordan Blekking ◽  
Oghenekaro Omodior
Keyword(s):  
Risk Factors ◽  
Land Management ◽  
Negative Binomial ◽  
Hot Spot ◽  
Negative Binomial Regression ◽  
Online Reviews ◽  
User Reviews ◽  
Binomial Regression ◽  
State Region ◽  
Recreational Trails

Abstract Background Recreational trails abound across the United States and represent high risk areas for tick exposure. Although online reviews represent a rich source of user information, they have rarely been used in determining the risk of tick exposure during recreational trail use. Based on online user reviews and comments, the purpose of this study was to determine risk factors and behavioral recommendations associated with tick encounters (Tick Presence) on recreational trails in the state of Indiana, U.S. Methods We reviewed 26,016 user comments left on AllTrails.com for 697 Indiana trails. Reviews were evaluated to determine Tick Presence/Absence, the total number of Tick Presence Reviews per trail, and multiple trail and user behavioral characteristics. We used hot spot (Getis-Ord Gi*) analysis to test the hypothesis of whether there are clusters in the number of Tick Presence Reviews. Pearson chi-square tests of independence evaluated whether tick presence was associated with several trail characteristics. Finally, negative binomial regression evaluated the strength of the association between the number of Tick Presence Reviews and several trail characteristics. Results Tick Presence was recorded at 10% (n = 65) of trails and occurred most frequently in May. Hot spot analysis revealed statistically significant clusters of Tick Presence Reviews on trails in the Southern Indiana State Region. Results of χ2 tests indicated significant associations between Tick Presence Reviews and (a) State Region and (b) Land Management Type; Mann-Whitney U tests detected significant differences in Tick Presence Reviews based on Trail Length and Elevation Gain. Subsequent results of a negative binomial regression model indicated that Southern Indiana State Region, Federal and Private Land Management Type, and Elevation Gain were factors significantly associated with Tick Presence Reviews. Content of user reviews indicated several behaviors employed to prevent tick encounters, particularly Repellent Application and Recreational Deterrence; 25% included a behavior Recommendation to others. Conclusions Online, user-generated trail reviews have the potential to serve as rich data sources for identifying recreational trails, where 1) the risk of tick exposure is great, 2) more robust active tick and tick-borne pathogen surveillance may be warranted, and 3) tailored prevention interventions are needed.

scholarly journals Municipality- level predictors of COVID-19 mortality in Mexico: a cautionary tale

2020 ◽  
pp. 1-23
Author(s):  
Alejandra Contreras-Manzano ◽  
Carlos M. Guerrero-López ◽  
Mercedes Aguerrebere ◽  
Ana Cristina Sedas ◽  
Héctor Lamadrid-Figueroa
Keyword(s):  
Mortality Rate ◽  
Population Density ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Obesity Prevalence ◽  
Factors Associated ◽  
Active Population ◽  
Binomial Regression ◽  
And Control

Abstract Objective Local characteristics of populations have been associated with COVID-19 outcomes. We analyze the Municipality-level factors associated with a high COVID-19 mortality rate of in Mexico. Methods We retrieved information from cumulative confirmed symptomatic cases and deaths of COVID-19 as of June 20th, 2020 and data from most recent census and surveys of Mexico. A negative binomial regression model was adjusted, dependent variable was the COVID-19 deaths and the independent variables were the quintiles of the distribution of sociodemographic and health characteristics among the 2,457 Municipalities of Mexico. Results Factors associated with high MR of COVID-19, relative to Quintile 1 were; diabetes and obesity prevalence, diabetes mortality rate, indigenous population, economically active population, density of economic units that operate essential activities and population density. Among factors inversely associated with lower MR of COVID-19 were; high hypertension prevalence and houses without drainage. We identified 1,351 municipalities without confirmed COVID-19 deaths, of which, 202 had high and 82 very high expected COVID-19 mortality (Means=8 and 13.8 deaths per 100,000 respectively). Conclusion This study identified Municipalities of Mexico that could lead to a high mortality scenario later in the epidemic and warns against premature easing of mobility restrictions and to reinforce strategies of prevention and control of outbreaks in communities vulnerable to COVID-19.

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