Identifying risk factors for contact injury in professional rugby league players – Application of a frailty model for recurrent injury

2012 ◽  
Vol 15 (6) ◽  
pp. 496-504 ◽  
Author(s):  
Tim J. Gabbett ◽  
Shahid Ullah ◽  
Caroline F. Finch
Keyword(s):  
Risk Factors ◽  
Frailty Model ◽  
Rugby League ◽  
Recurrent Injury

scholarly journals Statistical modelling for recurrent injury events: an application to Rugby League injuries

2010 ◽  
Vol 16 (Supplement 1) ◽  
pp. A191-A191
Author(s):  
S. Ullah ◽  
C. F. Finch ◽  
T. J. Gabbett
Keyword(s):  
Statistical Modelling ◽  
Rugby League ◽  
Recurrent Injury

scholarly journals Modeling Risk Factors Associated With Time to Pregnancy Termination in Ethiopia: Application of Frailty Models

2020 ◽  
Author(s):  
Aboma Temesgen Sebu
Keyword(s):  
Risk Factors ◽  
Pregnancy Termination ◽  
Survival Curve ◽  
Frailty Model ◽  
Survival Model ◽  
Frailty Models ◽  
Early Age ◽  
Time To Pregnancy ◽  
Factors Associated ◽  
Number Of Children

Abstract Background: Pregnancy termination commonly known as abortion is the preventable causes for the maternal mortality worldwide that largely forgotten. About 45 % of these pregnancy terminations are unsafe causing death of more than 22,000 women every year and remains major public health problems in developing countries including Ethiopia. This study was also aimed to model and investigate risk factors associated with time to pregnancy termination in Ethiopia by applying survival model considering the clustering effects.Methods: The study considered 15,683 reproductive age group women from 2016 Ethiopian Demographic and Health Survey data. Kaplan-Meier(KM) was employed to estimate the survival curve and this estimated KM survival curve estimated for different groups were tested based on log rank test. To come up with appropriate model for the time to pregnancy termination and the associated risk factors both semi-parametric and parametric survival model with no frailty effects as wells as with shared frailty effects which handles random effects were employed and compared based AIC and BIC of the fitted models.Results: The result of the study showed generalized gamma and lognormal survival models were appropriate models compared with semi-parametric and other candidate parametric models.Fitting these survival model with frailty showed the improvement of the models which was an indication for the presence of unobservable random effects in clusters. Regarding the frailty models comparison, log normal with gamma frailty model was considered as appropriate model for fitting time to pregnancy termination model in Ethiopia compared with other candidate frailty models. Furthermore, the selected frailty model result showed that age of women, ever trying to avoid pregnancy, contraceptive method use, age at first sex, total number of children ever born and place of residence were the identified risk factors for the time to pregnancy termination at 5% level of significance.Conclusions: Based on the finding of this study, starting sex at early age, residing urban areas, having lower number of children, being in married marital status group, chewing chat and do not using contraceptive methods were the risk factors that results pregnancy termination at early age that needs serious consideration to prevent the problem in Ethiopia.

scholarly journals Modeling the Determinants of Time-to-Premarital Cohabitation among Women of Ethiopia: A Comparison of Various Parametric Shared Frailty Models

2021 ◽  
Author(s):  
Woldemariam Erkalo Gobena
Keyword(s):  
Risk Factors ◽  
Frailty Model ◽  
Frailty Models ◽  
Wealth Status ◽  
Premarital Cohabitation ◽  
Shared Frailty Model ◽  
Shared Frailty ◽  
Education Status ◽  
Potential Risk Factors ◽  
Clustering Effect

Abstract Background: Premarital cohabitation is defined as the state of living together and having a sexual relationship without being married. It has become more prevalent globally in recent decades. The main objective of this study was modeling the potential risk factors of time-to-premarital cohabitation among women of Ethiopia by using parametric shared frailty models where regional states of the women were used as a clustering effect in the models.Methods: The data source for the analysis was the 2016 EDHS data. The Gamma and Inverse-Gaussian shared frailty distributions with Exponential, Weibull, Log-logistic and Lognormal baseline models were employed to analyze risk factors associated with age at premarital cohabitation. All the fitted models were compared by using AIC values.Results: The median age of women at premarital cohabitation was 18 years. Based on AIC values, Log-logistic-Gamma shared frailty model has smallest AIC value among the models compared. The clustering effect was significant for modeling the determinants of time-to-premarital cohabitation dataset. The results showed that women’s education status, occupation, pregnancy and place of residence were found to be the most significant determinants of age at premarital cohabitation whereas wealth status and religion were not significant at 5% level.Conclusions: The Log-logistic-Gamma shared frailty model described the premarital cohabitation dataset better than other distributions used in this study. There is heterogeneity between the regions of women. Further studies should be conducted to identify other factors of age at premarital cohabitation of women in Ethiopia that were not included in this study.

Importance of Various Training-Load Measures in Injury Incidence of Professional Rugby League Athletes

2017 ◽  
Vol 12 (6) ◽  
pp. 819-824 ◽  
Author(s):  
Heidi R. Thornton ◽  
Jace A. Delaney ◽  
Grant M. Duthie ◽  
Ben J. Dascombe
Keyword(s):  
Risk Factors ◽  
High Speed ◽  
Injury Risk ◽  
Perceived Exertion ◽  
Rugby League ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Machine Learning Techniques ◽  
Injury Incidence ◽  
The Individual

Purpose:To investigate the ability of various internal and external training-load (TL) monitoring measures to predict injury incidence among positional groups in professional rugby league athletes.Methods:TL and injury data were collected across 3 seasons (2013–2015) from 25 players competing in National Rugby League competition. Daily TL data were included in the analysis, including session rating of perceived exertion (sRPE-TL), total distance (TD), high-speed-running distance (>5 m/s), and high-metabolic-power distance (HPD; >20 W/kg). Rolling sums were calculated, nontraining days were removed, and athletes’ corresponding injury status was marked as “available” or “unavailable.” Linear (generalized estimating equations) and nonlinear (random forest; RF) statistical methods were adopted.Results:Injury risk factors varied according to positional group. For adjustables, the TL variables associated most highly with injury were 7-d TD and 7-d HPD, whereas for hit-up forwards they were sRPE-TL ratio and 14-d TD. For outside backs, 21- and 28-d sRPE-TL were identified, and for wide-running forwards, sRPE-TL ratio. The individual RF models showed that the importance of the TL variables in injury incidence varied between athletes.Conclusions:Differences in risk factors were recognized between positional groups and individual athletes, likely due to varied physiological capacities and physical demands. Furthermore, these results suggest that robust machine-learning techniques can appropriately monitor injury risk in professional team-sport athletes.

Predisposing risk factors for hamstring and quadriceps strain injury in male football and rugby league players

Physiotherapy ◽  
2015 ◽  
Vol 101 ◽  
pp. e406-e407
Author(s):  
P. Freeman ◽  
A. Miller ◽  
S. Snodgrass ◽  
R. Callister
Keyword(s):  
Risk Factors ◽  
Rugby League

scholarly journals Predicting Clustered Dental Implant Survival Using Frailty Methods

2006 ◽  
Vol 85 (12) ◽  
pp. 1147-1151 ◽  
Author(s):  
S.-K. Chuang ◽  
T. Cai
Keyword(s):  
Risk Factors ◽  
Clinical Decision Making ◽  
Proportional Hazards ◽  
Smoking Status ◽  
Joint Probability ◽  
Frailty Model ◽  
Clinical Decision ◽  
Cox Proportional Hazards ◽  
Implant Survival ◽  
Survival Status

The purpose of this study was to predict future implant survival using information on risk factors and on the survival status of an individual’s existing implant(s). We considered a retrospective cohort study with 677 individuals having 2349 implants placed. We proposed to predict the survival probabilities using the Cox proportional hazards frailty model, with three important risk factors: smoking status, timing of placement, and implant staging. For a non-smoking individual with 2 implants placed, an immediate implant and in one stage, the marginal probability that 1 implant would survive 12 months was 85.8% (95%CI: 77%, 91.7%), and the predicted joint probability of surviving for 12 months was 75.1% (95%CI: 62.1%, 84.7%). If 1 implant was placed earlier and had survived for 12 months, then the second implant had an 87.5% (95%CI: 80.3%, 92.4%) chance of surviving 12 months. Such conditional and joint predictions can assist in clinical decision-making for individuals.

Risk Factors for Injury in Subelite Rugby League Players

2005 ◽  
Vol 33 (3) ◽  
pp. 428-434 ◽  
Author(s):  
Tim J. Gabbett ◽  
Nathan Domrow
Keyword(s):  
Risk Factors ◽  
Physical Fitness ◽  
Aerobic Power ◽  
Vertical Jump ◽  
Rugby League ◽  
Maximal Aerobic Power ◽  
Level Of Evidence ◽  
Risk Of Injury ◽  
Increased Risk ◽  
Subsequent Injury

Background Although player fatigue and playing intensity have been suggested to contribute to injuries in rugby league players, no study has confirmed if the level of physical fitness is a risk factor for injury in rugby league players. The aim of this study was to identify risk factors for injury in subelite rugby league players. Hypothesis Low physical fitness levels are risk factors for injury in subelite rugby league players. Study Design Cohort study; Level of evidence, 2. Methods One hundred fifty-three players from a subelite rugby league club underwent preseason measurements of muscular power (vertical jump), speed (10- and 40-m sprint), and maximal aerobic power (multistage fitness test) over 4 competitive seasons. All injuries sustained by players were prospectively recorded over the 4 competitive seasons. Results The risk of injury was greater in players with low 10- and 40-m speed. Players with a low maximal aerobic power had a greater risk of sustaining a contact injury. In addition, players who completed less than 18 weeks of training before sustaining their initial injuries were at greater risk of sustaining a subsequent injury. Conclusions Subelite rugby league players with low speed and maximal aerobic power are at an increased risk of injury. In addition, players who complete less than 18 weeks of training before sustaining an initial injury are at greater risk of sustaining a subsequent injury. These findings highlight the importance of speed and endurance training to reduce the incidence of injury in subelite rugby league players.

scholarly journals A Spatial Survival Model for Risk Factors of Under-Five Child Mortality in Kenya

2021 ◽  
Vol 19 (1) ◽  
pp. 399
Author(s):  
Kilemi Daniel ◽  
Nelson Owuor Onyango ◽  
Rachel Jelagat Sarguta
Keyword(s):  
Risk Factors ◽  
Health Care ◽  
Spatial Variation ◽  
Child Mortality ◽  
Frailty Model ◽  
Proportional Hazard ◽  
Under Five ◽  
Cox Proportional Hazard ◽  
Death Of A Child ◽  
Spatial Differences

Child mortality is high in Sub-Saharan Africa compared to other regions in the world. In Kenya, the risk of mortality is assumed to vary from county to county due to diversity in socio-economic and even climatic factors. Recently, the country was split into 47 different administrative regions called counties, and health care was delegated to those county governments, further aggravating the spatial differences in health care from county to county. The goal of this study is to evaluate the effects of spatial variation in under-five mortality in Kenya. Data from the Kenya Demographic Health Survey (KDHS-2014) consisting the newly introduced counties was used to analyze this risk. Using a spatial Cox Proportional Hazard model, an Intrinsic Conditional Autoregressive Model (ICAR) was fitted to account for the spatial variation among the counties in the country while the Cox model was used to model the risk factors associated with the time to death of a child. Inference regarding the risk factors and the spatial variation was made in a Bayesian setup based on the Markov Chain Monte Carlo (MCMC) technique to provide posterior estimates. The paper indicate the spatial disparities that exist in the country regarding child mortality in Kenya. The specific counties have mortality rates that are county-specific, although neighboring counties have similar hazards for death of a child. Counties in the central Kenya region were shown to have the highest hazard of death, while those from the western region had the lowest hazard of death. Demographic factors such as the sex of the child and sex of the household head, as well as social economic factors, such as the level of education, accounted for the most variation when spatial differences were factored in. The spatial Cox proportional hazard frailty model performed better compared to the non-spatial non-frailty model. These findings can help the country to plan health care interventions at a subnational level and guide social and health policies by ensuring that counties with a higher risk of Under Five Child Mortality (U5CM) are considered differently from counties experiencing a lower risk of death.

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